Learning to summarize from human feedback
Nisan Stiennon∗ Long Ouyang∗ Jeff Wu∗ Daniel M. Ziegler∗ Ryan Lowe∗
Chelsea Voss∗ Alec Radford Dario Amodei Paul Christiano∗
arXiv:2009.01325v3 [cs.CL] 15 Feb 2022
OpenAI
Abstract
As language models become more powerful, training and evaluation are increas-
ingly bottlenecked by the data and metrics used for a particular task. For example,
summarization models are often trained to predict human reference summaries and
evaluated using ROUGE, but both of these metrics are rough proxies for what we
really care about—summary quality. In this work, we show that it is possible to
significantly improve summary quality by training a model to optimize for human
preferences. We collect a large, high-quality dataset of human comparisons be-
tween summaries, train a model to predict the human-preferred summary, and use
that model as a reward function to fine-tune a summarization policy using reinforce-
ment learning. We apply our method to a version of the TL;DR dataset of Reddit
posts [63] and find that our models significantly outperform both human reference
summaries and much larger models fine-tuned with supervised learning alone. Our
models also transfer to CNN/DM news articles [22], producing summaries nearly
as good as the human reference without any news-specific fine-tuning.2 We con-
duct extensive analyses to understand our human feedback dataset and fine-tuned
models.3 We establish that our reward model generalizes to new datasets, and that
optimizing our reward model results in better summaries than optimizing ROUGE
according to humans. We hope the evidence from our paper motivates machine
learning researchers to pay closer attention to how their training loss affects the
model behavior they actually want.
1 Introduction
Large-scale language model pretraining has become increasingly prevalent for achieving high per-
formance on a variety of natural language processing (NLP) tasks. When applying these models
to a specific task, they are usually fine-tuned using supervised learning, often to maximize the log
probability of a set of human demonstrations.
While this strategy has led to markedly improved performance, there is still a misalignment between
this fine-tuning objective—maximizing the likelihood of human-written text—and what we care
about—generating high-quality outputs as determined by humans. This misalignment has several
causes: the maximum likelihood objective has no distinction between important errors (e.g. making
up facts [41]) and unimportant errors (e.g. selecting the precise word from a set of synonyms); models
∗
This was a joint project of the OpenAI Reflection team. Author order was randomized amongst {LO, JW,
DZ, NS}; CV and RL were full-time contributors for most of the duration. PC is the team lead.
2
Samples from all of our models can be viewed on our website.
3
We provide inference code for our 1.3B models and baselines, as well as a model card and our human
feedback dataset with over 64k summary comparisons, here.
34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada.
Human feedback
Reference summaries
Supervised learning
Pretrain only
Figure 1: Fraction of the time humans prefer our models’ summaries over the human-generated
reference summaries on the TL;DR dataset.4 Since quality judgments involve an arbitrary decision
about how to trade off summary length vs. coverage within the 24-48 token limit, we also provide
length-controlled graphs in Appendix F; length differences explain about a third of the gap between
feedback and supervised learning at 6.7B.
are incentivized to place probability mass on all human demonstrations, including those that are
low-quality; and distributional shift during sampling can degrade performance [56, 52]. Quality can
often be improved significantly by non-uniform sampling strategies such as beam search [51], but
these can lead to repetition and other undesirable artifacts [69, 23]. Optimizing for quality may be a
principled approach to overcoming these problems.
Our goal in this paper is to advance methods for training language models on objectives that more
closely capture the behavior we care about. To make short-term progress towards this goal, we
focus on abstractive English text summarization, as it has a long history in the NLP community
[16, 8, 54, 59, 50], and is a subjective task where we believe it is difficult to quantify summary quality
without human judgments. Indeed, existing automatic metrics for evaluating summary quality, such
as ROUGE [39], have received criticism for poor correlation with human judgments [55, 45, 6, 33].
We follow the works of [3, 73], who fine-tune language models from human feedback using reward
learning [35]. We first collect a dataset of human preferences between pairs of summaries, then train
a reward model (RM) via supervised learning to predict the human-preferred summary. Finally, we
train a policy via reinforcement learning (RL) to maximize the score given by the RM; the policy
generates a token of text at each ‘time step’, and is updated using the PPO algorithm [58] based on
the RM ‘reward’ given to the entire generated summary. We can then gather more human data using
samples from the resulting policy, and repeat the process. We follow the works of [48, 4] and use
large pretrained GPT-3 models with as many as 6.7 billion parameters.
Our main contributions are four-fold.
(1) We show that training with human feedback significantly outperforms very strong baselines
on English summarization. When applying our methods on a version of the Reddit TL;DR dataset
[63], we train policies via human feedback that produce better summaries than much larger policies
trained via supervised learning. Summaries from our human feedback models are preferred by our
labelers to the original human demonstrations in the dataset (see Figure 1).
(2) We show human feedback models generalize much better to new domains than supervised
models. Our Reddit-trained human feedback models also generate high-quality summaries of news
articles on the CNN/DailyMail (CNN/DM) dataset without any news-specific fine-tuning, almost
matching the quality of the dataset’s reference summaries. We perform several checks to ensure
that these human preferences reflect a real quality difference: we consistently monitor agreement
rates amongst labelers and researchers, and find researcher-labeler agreement rates are nearly as high
as researcher-researcher agreement rates (see Section C.2), and we verify models are not merely
optimizing simple metrics like length or amount of copying (see Appendices F and G.7).
4
Throughout the paper, error bars represent 1 standard error.
2
(3) We conduct extensive empirical analyses of our policy and reward model. We examine the
impact of model and data size (Figure 6), study performance as we continue to optimize a given
reward model (Section 4.3), and analyze reward model performance using synthetic and human-
written perturbations of summaries (Section 4.3). We confirm that our reward model outperforms
other metrics such as ROUGE at predicting human preferences, and that optimizing our reward model
directly results in better summaries than optimizing ROUGE according to humans (Section 4.4).
(4) We publicly release our human feedback dataset for further research. The dataset contains
64,832 summary comparisons on the TL;DR dataset, as well as our evaluation data on both TL;DR
(comparisons and Likert scores) and CNN/DM (Likert scores).
The methods we present in this paper are motivated in part by longer-term concerns about the
misalignment of AI systems with what humans want them to do. When misaligned summarization
models make up facts, their mistakes are fairly low-risk and easy to spot. However, as AI systems
become more powerful and are given increasingly important tasks, the mistakes they make will likely
become more subtle and safety-critical, making this an important area for further research.
2 Related work
Most directly related to our work is previous work using human feedback to train summarization
models with RL [3, 73]. Bohm et al. [3] learn a reward function from a dataset of human ratings of
2.5k CNN/DM summaries, and train a policy whose summaries are preferred to a policy optimizing
ROUGE. Our work is most similar to [73], who also train Transformer models [62] to optimize human
feedback across a range of tasks, including summarization on the Reddit TL;DR and CNN/DM
datasets. Unlike us, they train in an online manner and find the model highly extractive. They
note that their labelers prefer extractive summaries and have low agreement rates with researchers.
Compared to [73], we use significantly larger models, move to the batch setting for collecting human
feedback, ensure high labeler-researcher agreement, and make some algorithmic modifications, such
as separating the policy and value networks.
Human feedback has also been used as a reward to train models in other domains such as dialogue
[25, 68, 21], translation [32, 1], semantic parsing [34], story generation [72], review generation
[7], and evidence extraction [46]. Our reward modeling approach was developed in prior work
on learning to rank [40], which has been applied to ranking search results using either explicit
feedback [2, 18] or implicit feedback in the form of click-through data [29, 30]. In a related line of
research, human feedback has been used to train agents in simulated environments [10, 24]. There
is also a rich literature on using RL to optimize automatic metrics for NLP tasks, such as ROUGE
for summarization [50, 65, 45, 15, 19], BLEU for translation [50, 66, 1, 43], and other domains
[61, 27, 26]. Finally, there has been extensive research on modifying architectures [22, 59] and
pre-training procedures [70, 36, 49, 60, 53, 14] for improving summarization performance.
3 Method and experiment details
3.1 High-level methodology
Our approach is similar to the one outlined in [73], adapted to the batch setting. We start with an
initial policy that is fine-tuned via supervised learning on the desired dataset (in our case, the Reddit
TL;DR summarization dataset). The process (illustrated in Figure 2) then consists of three steps that
can be repeated iteratively.
Step 1: Collect samples from existing policies and send comparisons to humans. For each
Reddit post, we sample summaries from several sources including the current policy, initial policy,
original reference summaries and various baselines. We send a batch of pairs of summaries to our
human evaluators, who are tasked with selecting the best summary of a given Reddit post.
Step 2: Learn a reward model from human comparisons. Given a post and a candidate summary,
we train a reward model to predict the log odds that this summary is the better one, as judged by our
labelers.
Step 3: Optimize a policy against the reward model. We treat the logit output of the reward model
as a reward that we optimize using reinforcement learning, specifically with the PPO algorithm [58].
3
1 Collect human feedback 2 Train reward model 3 Train policy with PPO
A Reddit post is One post with A new post is
sampled from two summaries sampled from the
the Reddit judged by a dataset.
TL;DR dataset. human are fed
to the reward
model.
j k
The policy π
Various policies The reward generates a
are used to model summary for the
sample a set of calculates a post. π r
summaries. reward r for
each summary. r r
Two summaries
are selected for
evaluation. rj rk
j k
The loss is The reward
calculated based model calculates
A human judges a reward for the
on the rewards
which is a better summary.
and human label, loss = log(σ(rj - rk ))
summary of the
and is used to
r
post.
update the
reward model. The reward is
used to update
the policy via
“j is better than k” “j is better than k” PPO. r
Figure 2: Diagram of our human feedback, reward model training, and policy training procedure.
We provide a more thorough description of our procedure, including details of the reward model and
policy training and our quality control process, in the following sections. In practice, rather than
precisely iterating this sequence of three steps, we updated our data collection and training procedures
over the course of the project while accumulating labels (see Appendix C.6 for details).
3.2 Datasets and task
Datasets. We use the TL;DR summarization dataset [63], which contains ~3 million posts from
reddit.com across a variety of topics (subreddits), as well summaries of the posts written by the
original poster (TL;DRs). We additionally filter this dataset (see Appendix A) to ensure quality,
including using a whitelist of subreddits that are understandable to the general population. Crucially,
we also filter to include only posts where the human-written summaries contain between 24 and
48 tokens, to minimize the potential effect of summary length on quality (see Section 4.1 and
Appendix F). Our final filtered dataset contains 123,169 posts, and we hold out ~5% as a validation
set. For the remainder of this paper, we refer to this dataset simply as TL;DR.
We chose the TL;DR dataset over the more commonly used CNN/DM dataset primarily because
very strong performance can be attained on CNN/DM with simple extractive baselines. We find in
Section 4.2 that our labelers prefer lead-3 over the CNN/DM reference summaries,5 and that the
supervised T5 model [49] with low-temperature sampling already surpasses the reference summary
quality, while copying extensively from the article. On the other hand, simple extractive baselines
perform poorly on TL;DR in our human evaluations (see Appendix G.2). Instead of training on
CNN/DM, we study the transfer performance of our human feedback models to CNN/DM after being
trained to summarize Reddit posts.
Task. We define our ground-truth task as producing a model that generates summaries fewer than
48 tokens long that are as good as possible, according to our judgments. We judge summary quality
by how faithfully the summary conveys the original post to a reader who can only read the summary
and not the post (see Appendix C.5 for further discussion of criteria). Since we have limited capacity
to do comparisons, we hire labelers to do the comparisons for us. We rely on detailed procedures to
ensure high agreement between labelers and us on the task, which we describe in the next section.
5
We manually check this result in Appendix E and find we generally agree with labeler ratings.
4
[r/dating_advice] First date ever, going to the beach. Would like some tips
Hey Reddit! I (20M) would like some tips, because I have my first ever date tomorrow (although I’ve had a gf for 3
years, but no actual dating happened), and we’re going to the beach.
I met this girl, we have mutual friends, at a festival a few days ago. We didn’t kiss, but we talked, held
hands, danced a bit. I asked her to go on a date with me, which was super hard as it is the first time I’ve asked this
to anybody. What I mean to say is, it’s not like a standard *first* date because we already spent some time together.
I’m really nervous and excited. I’m going to pick her up tomorrow, we’re cycling to the beach which will
take 30 minutes, and then what? I’m a bit scared. Should I bring something (the weather, although no rain and
sunny, is not super so no swimming), should we do something. I’d like all the tips I can get. Thanks!
Human written reference TL;DR 6.7B supervised model 6.7B human feedback model
First date after 3 years in a relation- Going on a date with a girl I met Going on my first ever date tomor-
ship, going to the beach, terrified. a few days ago, going to the beach. row, cycling to the beach. Would
What to bring with me, what to do? What should I bring, what should like some tips on what to do and
we do? bring. I’m a bit nervous and excited.
Thanks!
Table 1: Example of post and samples on the TL;DR dataset, chosen to be particularly short. For
random samples (along with posts), see Appendix H and our website.
3.3 Collecting human feedback
Previous work on fine-tuning language models from human feedback [73] reported “a mismatch
between the notion of quality we wanted our model to learn, and what the humans labelers actually
evaluated”, leading to model-generated summaries that were high-quality according to the labelers,
but fairly low-quality according to the researchers.
Compared to [73], we implement two changes to improve human data quality. First, we transition
entirely to the offline setting, where we alternate between sending large batches of comparison data6
to our human labelers and re-training our models on the cumulative collected data. Second, we
maintain a hands-on relationship with labelers:7 we on-board them with detailed instructions, answer
their questions in a shared chat room, and provide regular feedback on their performance. We train all
labelers to ensure high agreement with our judgments, and continuously monitor labeler-researcher
agreement over the course of the project. See Appendix C.1 and C.5 for details.
As a result of our procedure, we obtained high labeler-researcher agreement: on a subset of compari-
son tasks, labelers agree with researchers 77% ± 2% of the time, while researchers agree with each
other 73% ± 4% of the time. We provide more analysis of our human data quality in Appendix C.2.
3.4 Models
All of our models are Transformer decoders [62] in the style of GPT-3 [47, 4]. We conduct our human
feedback experiments on models with 1.3 billion (1.3B) and 6.7 billion (6.7B) parameters.
Pretrained models. Similarly to [12, 47], we start with models pretrained to autoregressively
predict the next token in a large text corpus. As in [48, 4], we use these models as ‘zero-shot’
baselines by padding the context with examples of high-quality summaries from the dataset. We
provide details on pretraining in Appendix B, and on our zero-shot procedure in Appendix B.2.
Supervised baselines. We next fine-tune these models via supervised learning to predict summaries
from our filtered TL;DR dataset (see Appendix B for details). We use these supervised models to
sample initial summaries for collecting comparisons, to initialize our policy and reward models, and
as baselines for evaluation. In our final human evaluations, we use T=0 to sample from all models, as
we found it performed better than higher temperatures or nucleus sampling (see Appendix B.1).
To validate that our supervised models are indeed strong baselines for comparison, we run our
supervised fine-tuning procedure with our 6.7B model on the CNN/DM dataset, and find that we
achieve slightly better ROUGE scores than SOTA models [71] from mid-2019 (see Appendix G.4).
6
Our decision to collect comparisons rather than Likert scores is supported by recent work, e.g. [37].
7
We recruited labelers from a freelancing platform, Upwork, and two labeling services, Scale and Lionbridge.
5
Reward models. To train our reward models, we start from a supervised baseline, as described
above, then add a randomly initialized linear head that outputs a scalar value. We train this model to
predict which summary y ∈ {y0 , y1 } is better as judged by a human, given a post x. If the summary
preferred by the human is yi , we can write the RM loss as:
loss(rθ ) = −E(x,y0 ,y1 ,i)∼D [log(σ(rθ (x, yi ) − rθ (x, y1−i )))]
where rθ (x, y) is the scalar output of the reward model for post x and summary y with parameters θ,
and D is the dataset of human judgments. At the end of training, we normalize the reward model
outputs such that the reference summaries from our dataset achieve a mean score of 0.
Human feedback policies. We want to use the reward model trained above to train a policy that
generates higher-quality outputs as judged by humans. We primarily do this using reinforcement
learning, by treating the output of the reward model as a reward for the entire summary that we
maximize with the PPO algorithm [58], where each time step is a BPE token.8 We initialize our
policy to be the model fine-tuned on Reddit TL;DR. Importantly, we include a term in the reward that
penalizes the KL divergence between the learned RL policy πφRL with parameters φ and this original
supervised model π SFT , as previously done in [25]. The full reward R can be written as:
R(x, y) = rθ (x, y) − β log[πφRL (y|x)/π SFT (y|x)]
This KL term serves two purposes. First, it acts as an entropy bonus, encouraging the policy to
explore and deterring it from collapsing to a single mode. Second, it ensures the policy doesn’t learn
to produce outputs that are too different from those that the reward model has seen during training.
For the PPO value function, we use a Transformer with completely separate parameters from the
policy. This prevents updates to the value function from partially destroying the pretrained policy
early in training (see ablation in Appendix G.1). We initialize the value function to the parameters of
the reward model. In our experiments, the reward model, policy, and value function are the same size.
4 Results
4.1 Summarizing Reddit posts from human feedback
Policies trained with human feedback are preferred to much larger supervised policies. Our
main results evaluating our human feedback policies on TL;DR are shown in Figure 1. We measure
policy quality as the percentage of summaries generated by that policy that humans prefer over
the reference summaries in the dataset. Our policies trained with human feedback significantly
outperform our supervised baselines on this metric, with our 1.3B human feedback model significantly
outperforming a supervised model 10× its size (61% versus 43% raw preference score against
reference summaries). Our 6.7B model in turn significantly outperforms our 1.3B model, suggesting
that training with human feedback also benefits from scale. Additionally, both of our human feedback
models are judged by humans to be superior to the human demonstrations used in the dataset.
Controlling for summary length. When judging summary quality, summary length is a confound-
ing factor. The target length of a summary is implicitly part of the summarization task; depending on
the desired trade-off between conciseness and coverage, a shorter or longer summary might be better.
Since our models learned to generate longer summaries, length could account for much of our quality
improvements. We find that after controlling for length (Appendix F), the preference of our human
feedback models vs. reference summaries drops by ~5%; even so, our 6.7B model summaries are still
preferred to the reference summaries ~65% of the time.
How do our policies improve over the baselines? To better understand the quality of our models’
summaries compared to the reference summaries and those of our supervised baselines, we conduct
an additional analysis where human labelers assess summary quality across four dimensions (or
“axes”) using a 7-point Likert scale [38]. Labelers rated summaries for coverage (how much important
information from the original post is covered), accuracy (to what degree the statements in the summary
are stated in the post), coherence (how easy the summary is to read on its own), and overall quality.
8
Note that the reward model only gives rewards for entire summaries, and not at intermediate time steps. In
RL terminology, each episode terminates when the policy outputs the EOS token, and the discount factor γ = 1.
6
Lead-3
T5 CNN/DM
finetuning
Reference summaries Supervised
CNN/DM
Human feedback
transfer
Pretrain
only
Supervised
transfer
(a) (b)
Figure 4: Transfer results on CNN/DM. (a) Overall summary quality on CNN/DM as a function of
model size. Full results across axes shown in Appendix G.2. (b) Overall scores vs. length for the
6.7B TL;DR supervised baseline, the 6.7B TL;DR human feedback model, and T5 fine-tuned on
CNN/DM summaries. At similar summary lengths, our 6.7B TL;DR human feedback model nearly
matches T5 despite never being trained to summarize news articles.
The results (Figure 3) indicate that our human feedback
models outperform the supervised baselines across every
dimension of quality, but particularly coverage. Although
our human labelers had a high bar for giving perfect overall
scores, summaries from our 6.7B PPO model achieve a 7/7
overall score 45% of the time (compared to 20% and 23%
for the 6.7B supervised baseline and reference summaries,
respectively).
4.2 Transfer to summarizing news articles
Our human feedback models can also generate excellent
summaries of CNN/DM news articles without any further
training (Figure 4). Our human feedback models signifi- Figure 3: Evaluations of four axes of
cantly outperform models trained via supervised learning summary quality on the TL;DR dataset.
on TL;DR and models trained only on pretraining corpora.
In fact, our 6.7B human feedback model performs almost as well as a 6.7B model that was fine-tuned
on the CNN/DM reference summaries, despite generating much shorter summaries.
Since our human feedback models transferred to CNN/DM have little overlap in summary length
distribution with models trained on CNN/DM, with about half as many tokens on average, they are
difficult to compare directly. Thus our evaluations in Figure 4 use a 7-point Likert scale on four
quality dimensions, as in Section 4.1 (see Appendix C.5 for labeler instructions). In Figure 4b we
show the average overall score at different summary lengths, which suggests our human feedback
models would perform even better if they generated longer summaries. Qualitatively, CNN/DM
summaries from our human feedback models are consistently fluent and reasonable representations
of the article; we show examples on our website and in Appendix H.
4.3 Understanding the reward model
What happens as we optimize the reward model? Optimizing against our reward model is
supposed to make our policy align with human preferences. But the reward model isn’t a perfect
representation of our labeler preferences, as it has limited capacity and only sees a small amount of
comparison data from a relatively narrow distribution of summaries. While we can hope our reward
model generalizes to summaries unseen during training, it’s unclear how much one can optimize
against the reward model until it starts giving useless evaluations.
To answer this question, we created a range of policies optimized against an earlier version of our
reward model, with varying degrees of optimization strength, and asked labelers to compare samples
from them to the reference summaries. Figure 5 shows the results for PPO at a range of KL penalty
7
Ensemble of humans
RM prediction
Human baseline
64k
32k
16k
8k
Actual preference
Figure 5: Preference scores versus degree of Figure 6: Reward model performance versus
reward model optimization. Optimizing against data size and model size. Doubling amount of
the reward model initially improves summaries, training data leads to a ~1.1% increase in reward
but eventually overfits, giving worse summaries. model validation accuracy, whereas doubling
This figure uses an earlier version of our reward the model size leads to a ~1.8% increase. The
model (see rm3 in Appendix C.6). See Appendix 6.7B model trained on all data begins approach-
H.2 for samples from the KL 250 model. ing the accuracy of a single human.
coefficients (β). Under light optimization, the models improve (according to labelers). However, as
we optimize further, true preferences fall off compared to the prediction, and eventually the reward
model becomes anti-correlated with human preferences. Though this is clearly undesirable, we note
that this over-optimization also happens with ROUGE (see [45] and Appendix G.3). Similar behavior
has been observed in learned reward functions in the robotics domain [5].
How does reward modeling scale with increasing model and data size? We conduct an ablation
to determine how data quantity and model size affect reward modeling performance. We train 7
reward models ranging from 160M to 13B parameters, on 8k to 64k human comparisons from our
dataset. We find that doubling the training data amount leads to a ~1.1% increase in the reward model
validation set accuracy, whereas doubling the model size leads to a ~1.8% increase (Figure 6).
What has the reward model learned? We probe our reward model by evaluating it on several
validation sets. We show the full results in Appendix G.6, and highlight them here. We find that our
reward models generalize to evaluating CNN/DM summaries (Appendix G.7), agreeing with labeler
preferences 62.4% and 66.5% of the time (for our 1.3B and 6.7B models, respectively). Our 6.7B
reward model nearly matches the inter-labeler agreement value of 66.9%.
We also find that our reward models are sensitive to small but semantically important details in
the summary. We construct an additional validation set by having labelers make minimal edits to
summaries to improve them. Our RMs prefer the edited summaries almost as often (79.4% for 1.3B
and 82.8% for 6.7B) as a separate set of human evaluators (84.1%). Further, when comparing the
reference summaries to perturbed summaries where the participants’ roles are reversed, our models
reliably select the original summary (92.9% of the time for 1.3B, 97.2% for 6.7B). However, our RMs
are biased towards longer summaries: our 6.7B RM prefers improving edits that make the summary
shorter only 62.6% of the time (vs. 76.4% for humans).
4.4 Analyzing automatic metrics for summarization
Evaluation. We study how well various automatic metrics act as predictors for human preferences,
and compare them to our RMs. Specifically, we examine ROUGE, summary length, amount of
copying from the post,9 and log probability under our baseline supervised models. We present a full
matrix of agreement rates between these metrics in Appendix G.7.
We find that our learned reward models consistently outperform other metrics, even on the CNN/DM
dataset on which it was never trained. We also find that ROUGE fails to track sample quality as our
9
We measure copying by computing the longest common subsequence of bigrams with the original Reddit
post or news article, and dividing by the number of bigrams in the summary.
8
Figure 7: Summary quality as a function of metric optimized and amount of optimization, using
best-of-N rejection sampling. We evaluate ROUGE, our main reward models, and an earlier iteration
of the 1.3B model trained on approximately 75% as much data (see Table 11 for details). ROUGE
appears to peak both sooner and at a substantially lower preference rate than all reward models.
Details in Appendix G.3.
models improve. While ROUGE has ~57% agreement with labelers when comparing samples from
our supervised baseline models, this drops to ~50% for samples from our human feedback model.
Similarly, log probability agreement with humans drops to ≤50% on comparisons between samples
from our human feedback models, while our RMs still perform above chance (62%). Scaling up the
size of the supervised model does not reliably improve log probability’s agreement with labelers.
Optimization. In Figure 7, we show that optimizing ROUGE using a simple optimization scheme
doesn’t consistently increase quality, as has been noted in [45]. Optimization against ROUGE peaks
both sooner and at a substantially lower quality rate than optimization against our reward models.
5 Discussion
Limitations. One limitation of our work is the time and cost required to produce our final models.
Notably, fine-tuning our 6.7B model with RL required approximately 320 GPU-days. Our data
collection procedure is also expensive compared to prior work — the training set took thousands of
labeler hours and required significant researcher time to ensure quality. For this reason, we were
unable to collect baselines such as an equivalent amount of high-quality human demonstrations for
supervised baselines. See D for more discussion. We leave this ablation to future work. Nevertheless,
we believe reward modeling is more likely to scale to tasks where it is extremely skill-intensive or
time-consuming to provide good demonstrations.
Future directions. The methods in this paper could be applied to any task where humans can
compare samples, including dialogue, machine translation, question answering, speech synthesis, and
music generation. We expect this method to be particularly important for generating long samples,
where the distributional shift and degeneracy of maximum likelihood samples can be problematic. It
may be possible to improve sample efficiency by training to predict feedback across many tasks [42].
We are particularly interested in scaling human feedback to tasks where humans can’t easily evaluate
the quality of model outputs. In this setting, it is particularly challenging to identify whether an ML
system is aligned with the human designer’s intentions. One approach is to train ML systems to help
humans perform the evaluation task quickly and accurately [9].
There is also a rich landscape of human feedback methods beyond binary comparisons that could be
explored for training models [28, 17, 44, 64]. For example, we could solicit high-quality demonstra-
tions from labelers, have labelers edit model outputs to make them better, or have labelers provide
explanations for why they preferred one model output over another. All of this feedback could be
leveraged as a signal to train more capable reward models and policies.
9
Broader impacts. The techniques we explore in this paper are generic techniques that could be
used in a wide variety of machine learning applications, for any task where it is feasible for humans
to evaluate the quality of model outputs. Thus, the potential implications are quite broad.
Our research is primarily motivated by the potential positive effects of aligning machine learning
algorithms with the designer’s preferences. Many machine learning applications optimize simple
metrics which are only rough proxies for what the designer intends. This can lead to problems, such
as Youtube recommendations promoting click-bait [11]. In the short term, improving techniques for
learning from and optimizing human preferences directly may enable these applications to be more
aligned with human well-being.
In the long term, as machine learning systems become more capable it will likely become increasingly
difficult to ensure that they are behaving safely: the mistakes they make might be more difficult to
spot, and the consequences will be more severe. For instance, writing an inaccurate summary of a
news article is both easy to notice (one simply has to read the original article) and has fairly low
consequences. On the other hand, imitating human driving may be substantially less safe than driving
to optimize human preferences. We believe that the techniques we explore in this paper are promising
steps towards mitigating the risks from such capable systems, and better aligning them with what
humans care about.
Unfortunately, our techniques also enable malicious actors to more easily train models that cause
societal harm. For instance, one could use human feedback to fine-tune a language model to be more
persuasive and manipulate humans’ beliefs, or to induce dependence of humans on the technology, or
to generate large amounts of toxic or hurtful content intended to harm specific individuals. Avoiding
these outcomes is a significant challenge for which there are few obvious solutions.
Large-scale models trained with human feedback could have significant impacts on many groups.
Thus, it is important to be careful about how we define the ‘good’ model behavior that human labelers
will reinforce. Deciding what makes a good summary is fairly straightforward, but doing this for
tasks with more complex objectives, where different humans might disagree on the correct model
behavior, will require significant care. In these cases, it is likely not appropriate to use researcher
labels as the ‘gold standard’; rather, individuals from groups impacted by the technology should be
included in the process to define ‘good’ behavior, and hired as labelers to reinforce this behavior in
the model.
We chose to train on the Reddit TL;DR dataset because the summarization task is significantly more
challenging than on CNN/DM. However, since the dataset consists of user-submitted posts with
minimal moderation, they often contain content that is offensive or reflects harmful social biases.
This means our models can generate biased or offensive summaries, as they have been trained to
summarize such content. For this reason, we recommend that the potential harms of our models be
thoroughly studied before deploying them in user-facing applications.
Finally, by improving the ability of machine learning algorithms to perform tasks that were previously
only achievable by humans, we are increasing the likelihood of many jobs being automated, potentially
leading to significant job loss. Without suitable policies targeted at mitigating the effects of large-scale
unemployment, this could also lead to significant societal harm.
Acknowledgements
We’d like to thank Beth Barnes for help with labeler hiring and general encouragement; Geoffrey
Irving for guidance on earlier iterations of the project and inspiring conversations; Ben Mann, Tom
Brown, Nick Ryder, and Melanie Subbiah for training and evaluating our pretrained models; Chris
Hesse, Eric Sigler, Benjamin Chess, Christopher Berner, Clemens Winter, Mateusz Litwin, and many
others for supporting us through computing infrastructure improvements and maintenance; Scott
Gray for writing fast GPU kernels; Arvind Neelakantan and Wojciech Kryscinski for discussions on
how to present the work, experiment design, and what datasets to use; Shan Carter for help designing
the main diagram; Douwe Kiela, Zach Lipton, and Alex Irpan for providing feedback on the paper;
and Gretchen Krueger for co-writing the model card accompanying the paper.
Finally, we’d like to thank all of our contractors for providing the data that was essential for training
the models in this paper, including: Emill Jayson Caypuno, Rachelle Froyalde, Cyra Denura, Alex
Malek, Isik Agil, Reshmi Patel, William Yap, Natalie Silver, Erol Akbaba, Jennifer Brillo, Alexandra
10
Uifalean, Morris Stuttard, Russell Bernandez, Tasmai Dave, Rachel Wallace, Jenny Fletcher, Jian
Ouyang, Justin Dill, Maria Orzek, Megan Niffenegger, William Sells, Emily Mariner, Andrew Seely,
Lychelle Ignacio, Jelena Ostojic, Nhan Tran, Purev Batdelgar, Valentina Kezic, Michelle Wilkerson,
Kelly Guerrero, Heather Scott, Sarah Mulligan, Gabriel Ricafrente, Kara Bell, Gabriel Perez, and
Alfred Lee.
References
[1] D. Bahdanau, P. Brakel, K. Xu, A. Goyal, R. Lowe, J. Pineau, A. Courville, and Y. Bengio. An
actor-critic algorithm for sequence prediction. arXiv preprint arXiv:1607.07086, 2016.
[2] B. T. Bartell, G. W. Cottrell, and R. K. Belew. Automatic combination of multiple ranked
retrieval systems. In SIGIR’94, pages 173–181. Springer, 1994.
[3] F. Böhm, Y. Gao, C. M. Meyer, O. Shapira, I. Dagan, and I. Gurevych. Better rewards yield
better summaries: Learning to summarise without references. arXiv preprint arXiv:1909.01214,
2019.
[4] T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan,
P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child,
A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray,
B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei.
Language models are few-shot learners. 2020.
[5] S. Cabi, S. Gómez Colmenarejo, A. Novikov, K. Konyushkova, S. Reed, R. Jeong, K. Zolna,
Y. Aytar, D. Budden, M. Vecerik, et al. Scaling data-driven robotics with reward sketching and
batch reinforcement learning. arXiv, pages arXiv–1909, 2019.
[6] A. T. Chaganty, S. Mussman, and P. Liang. The price of debiasing automatic metrics in natural
language evaluation. arXiv preprint arXiv:1807.02202, 2018.
[7] W. S. Cho, P. Zhang, Y. Zhang, X. Li, M. Galley, C. Brockett, M. Wang, and J. Gao. Towards
coherent and cohesive long-form text generation. arXiv preprint arXiv:1811.00511, 2018.
[8] S. Chopra, M. Auli, and A. M. Rush. Abstractive sentence summarization with attentive
recurrent neural networks. In Proceedings of the 2016 Conference of the North American
Chapter of the Association for Computational Linguistics: Human Language Technologies,
pages 93–98, 2016.
[9] P. Christiano, B. Shlegeris, and D. Amodei. Supervising strong learners by amplifying weak
experts. arXiv preprint arXiv:1810.08575, 2018.
[10] P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei. Deep reinforcement
learning from human preferences. In Advances in Neural Information Processing Systems,
pages 4299–4307, 2017.
[11] P. Covington, J. Adams, and E. Sargin. Deep neural networks for youtube recommendations. In
Proceedings of the 10th ACM conference on recommender systems, pages 191–198, 2016.
[12] A. M. Dai and Q. V. Le. Semi-supervised sequence learning. In Advances in neural information
processing systems, pages 3079–3087, 2015.
[13] J. Dodge, G. Ilharco, R. Schwartz, A. Farhadi, H. Hajishirzi, and N. Smith. Fine-tuning
pretrained language models: Weight initializations, data orders, and early stopping. arXiv
preprint arXiv:2002.06305, 2020.
[14] L. Dong, N. Yang, W. Wang, F. Wei, X. Liu, Y. Wang, J. Gao, M. Zhou, and H.-W. Hon. Unified
language model pre-training for natural language understanding and generation. In Advances in
Neural Information Processing Systems, 2019.
[15] Y. Dong, Y. Shen, E. Crawford, H. van Hoof, and J. C. K. Cheung. Banditsum: Extractive
summarization as a contextual bandit. arXiv preprint arXiv:1809.09672, 2018.
[16] B. Dorr, D. Zajic, and R. Schwartz. Hedge trimmer: A parse-and-trim approach to headline
generation. In Proceedings of the HLT-NAACL 03 on Text summarization workshop-Volume 5,
pages 1–8. Association for Computational Linguistics, 2003.
[17] S. Fidler et al. Teaching machines to describe images with natural language feedback. In
Advances in Neural Information Processing Systems, pages 5068–5078, 2017.
11
[18] N. Fuhr. Optimum polynomial retrieval functions based on the probability ranking principle.
ACM Transactions on Information Systems (TOIS), 7(3):183–204, 1989.
[19] Y. Gao, C. M. Meyer, M. Mesgar, and I. Gurevych. Reward learning for efficient reinforcement
learning in extractive document summarisation. arXiv preprint arXiv:1907.12894, 2019.
[20] X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural
networks. In Proceedings of the thirteenth international conference on artificial intelligence
and statistics, pages 249–256, 2010.
[21] B. Hancock, A. Bordes, P.-E. Mazare, and J. Weston. Learning from dialogue after deployment:
Feed yourself, chatbot! arXiv preprint arXiv:1901.05415, 2019.
[22] K. M. Hermann, T. Kocisky, E. Grefenstette, L. Espeholt, W. Kay, M. Suleyman, and P. Blunsom.
Teaching machines to read and comprehend. In Advances in neural information processing
systems, pages 1693–1701, 2015.
[23] A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi. The curious case of neural text
degeneration. arXiv preprint arXiv:1904.09751, 2019.
[24] B. Ibarz, J. Leike, T. Pohlen, G. Irving, S. Legg, and D. Amodei. Reward learning from human
preferences and demonstrations in atari. In Advances in neural information processing systems,
pages 8011–8023, 2018.
[25] N. Jaques, A. Ghandeharioun, J. H. Shen, C. Ferguson, A. Lapedriza, N. Jones, S. Gu, and
R. Picard. Way off-policy batch deep reinforcement learning of implicit human preferences in
dialog. arXiv preprint arXiv:1907.00456, 2019.
[26] N. Jaques, S. Gu, D. Bahdanau, J. M. Hernández-Lobato, R. E. Turner, and D. Eck. Sequence
tutor: Conservative fine-tuning of sequence generation models with kl-control. In International
Conference on Machine Learning, pages 1645–1654. PMLR, 2017.
[27] N. Jaques, S. Gu, R. E. Turner, and D. Eck. Tuning recurrent neural networks with reinforcement
learning. 2017.
[28] H. J. Jeon, S. Milli, and A. D. Dragan. Reward-rational (implicit) choice: A unifying formalism
for reward learning. arXiv preprint arXiv:2002.04833, 2020.
[29] T. Joachims. Optimizing search engines using clickthrough data. In Proceedings of the eighth
ACM SIGKDD international conference on Knowledge discovery and data mining, pages
133–142, 2002.
[30] T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting click-
through data as implicit feedback. In ACM SIGIR Forum, volume 51, pages 4–11. Acm New
York, NY, USA, 2005.
[31] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv preprint
arXiv:1412.6980, 2014.
[32] J. Kreutzer, S. Khadivi, E. Matusov, and S. Riezler. Can neural machine translation be improved
with user feedback? arXiv preprint arXiv:1804.05958, 2018.
[33] W. Kryscinski, N. S. Keskar, B. McCann, C. Xiong, and R. Socher. Neural text summarization:
A critical evaluation. In Proceedings of the 2019 Conference on Empirical Methods in Nat-
ural Language Processing and the 9th International Joint Conference on Natural Language
Processing (EMNLP-IJCNLP), pages 540–551, 2019.
[34] C. Lawrence and S. Riezler. Improving a neural semantic parser by counterfactual learning
from human bandit feedback. arXiv preprint arXiv:1805.01252, 2018.
[35] J. Leike, D. Krueger, T. Everitt, M. Martic, V. Maini, and S. Legg. Scalable agent alignment via
reward modeling: a research direction. arXiv preprint arXiv:1811.07871, 2018.
[36] M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and
L. Zettlemoyer. Bart: Denoising sequence-to-sequence pre-training for natural language
generation, translation, and comprehension. arXiv preprint arXiv:1910.13461, 2019.
[37] M. Li, J. Weston, and S. Roller. Acute-eval: Improved dialogue evaluation with optimized
questions and multi-turn comparisons. arXiv preprint arXiv:1909.03087, 2019.
[38] R. Likert. A technique for the measurement of attitudes. Archives of psychology, 1932.
12
[39] C.-Y. Lin and F. J. Och. Automatic evaluation of machine translation quality using longest
common subsequence and skip-bigram statistics. In Proceedings of the 42nd Annual Meeting on
Association for Computational Linguistics, page 605. Association for Computational Linguistics,
2004.
[40] T.-Y. Liu. Learning to rank for information retrieval. Springer Science & Business Media,
2011.
[41] J. Maynez, S. Narayan, B. Bohnet, and R. McDonald. On faithfulness and factuality in
abstractive summarization, 2020.
[42] B. McCann, N. S. Keskar, C. Xiong, and R. Socher. The natural language decathlon: Multitask
learning as question answering. arXiv preprint arXiv:1806.08730, 2018.
[43] K. Nguyen, H. Daumé III, and J. Boyd-Graber. Reinforcement learning for bandit neural
machine translation with simulated human feedback. arXiv preprint arXiv:1707.07402, 2017.
[44] T. Niu and M. Bansal. Polite dialogue generation without parallel data. Transactions of the
Association for Computational Linguistics, 6:373–389, 2018.
[45] R. Paulus, C. Xiong, and R. Socher. A deep reinforced model for abstractive summarization.
arXiv preprint arXiv:1705.04304, 2017.
[46] E. Perez, S. Karamcheti, R. Fergus, J. Weston, D. Kiela, and K. Cho. Finding generalizable
evidence by learning to convince q&a models. arXiv preprint arXiv:1909.05863, 2019.
[47] A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever. Improving language under-
standing by generative pre-training. URL https://s3-us-west-2. amazonaws. com/openai-
assets/researchcovers/languageunsupervised/language understanding paper. pdf, 2018.
[48] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever. Language models are
unsupervised multitask learners. OpenAI Blog, 1(8):9, 2019.
[49] C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu.
Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint
arXiv:1910.10683, 2019.
[50] M. Ranzato, S. Chopra, M. Auli, and W. Zaremba. Sequence level training with recurrent neural
networks. arXiv preprint arXiv:1511.06732, 2015.
[51] D. R. Reddy et al. Speech understanding systems: A summary of results of the five-year
research effort. department of computer science, 1977.
[52] S. Ross, G. Gordon, and D. Bagnell. A reduction of imitation learning and structured prediction
to no-regret online learning. In Proceedings of the fourteenth international conference on
artificial intelligence and statistics, pages 627–635, 2011.
[53] S. Rothe, S. Narayan, and A. Severyn. Leveraging pre-trained checkpoints for sequence
generation tasks. Transactions of the Association for Computational Linguistics, 2020.
[54] A. M. Rush, S. Chopra, and J. Weston. A neural attention model for abstractive sentence
summarization. arXiv preprint arXiv:1509.00685, 2015.
[55] N. Schluter. The limits of automatic summarisation according to rouge. In Proceedings of the
15th Conference of the European Chapter of the Association for Computational Linguistics:
Volume 2, Short Papers, pages 41–45, 2017.
[56] F. Schmidt. Generalization in generation: A closer look at exposure bias. arXiv preprint
arXiv:1910.00292, 2019.
[57] J. Schulman, P. Moritz, S. Levine, M. Jordan, and P. Abbeel. High-dimensional continuous
control using generalized advantage estimation. In Proceedings of the International Conference
on Learning Representations (ICLR), 2016.
[58] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov. Proximal policy optimization
algorithms. arXiv preprint arXiv:1707.06347, 2017.
[59] A. See, P. J. Liu, and C. D. Manning. Get to the point: Summarization with pointer-generator
networks. arXiv preprint arXiv:1704.04368, 2017.
[60] K. Song, X. Tan, T. Qin, J. Lu, and T.-Y. Liu. Mass: Masked sequence to sequence pre-training
for language generation. arXiv preprint arXiv:1905.02450, 2019.
13
[61] P. Tambwekar, M. Dhuliawala, A. Mehta, L. J. Martin, B. Harrison, and M. O. Riedl. Con-
trollable neural story generation via reinforcement learning. arXiv preprint arXiv:1809.10736,
2018.
[62] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and
I. Polosukhin. Attention is all you need. In Advances in neural information processing systems,
pages 5998–6008, 2017.
[63] M. Völske, M. Potthast, S. Syed, and B. Stein. Tl; dr: Mining reddit to learn automatic
summarization. In Proceedings of the Workshop on New Frontiers in Summarization, pages
59–63, 2017.
[64] S. Welleck, I. Kulikov, S. Roller, E. Dinan, K. Cho, and J. Weston. Neural text generation with
unlikelihood training. arXiv preprint arXiv:1908.04319, 2019.
[65] Y. Wu and B. Hu. Learning to extract coherent summary via deep reinforcement learning. In
Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
[66] Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao,
K. Macherey, et al. Google’s neural machine translation system: Bridging the gap between
human and machine translation. arXiv preprint arXiv:1609.08144, 2016.
[67] Y. Yan, W. Qi, Y. Gong, D. Liu, N. Duan, J. Chen, R. Zhang, and M. Zhou. Prophetnet: Pre-
dicting future n-gram for sequence-to-sequence pre-training. arXiv preprint arXiv:2001.04063,
2020.
[68] S. Yi, R. Goel, C. Khatri, A. Cervone, T. Chung, B. Hedayatnia, A. Venkatesh, R. Gabriel,
and D. Hakkani-Tur. Towards coherent and engaging spoken dialog response generation using
automatic conversation evaluators. arXiv preprint arXiv:1904.13015, 2019.
[69] H. Zhang, D. Duckworth, D. Ippolito, and A. Neelakantan. Trading off diversity and quality in
natural language generation. arXiv preprint arXiv:2004.10450, 2020.
[70] J. Zhang, Y. Zhao, M. Saleh, and P. J. Liu. Pegasus: Pre-training with extracted gap-sentences
for abstractive summarization. arXiv preprint arXiv:1912.08777, 2019.
[71] Y. Zhang, D. Li, Y. Wang, Y. Fang, and W. Xiao. Abstract text summarization with a convolu-
tional seq2seq model. Applied Sciences, 9(8):1665, 2019.
[72] W. Zhou and K. Xu. Learning to compare for better training and evaluation of open domain
natural language generation models. arXiv preprint arXiv:2002.05058, 2020.
[73] D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. Christiano, and G. Irv-
ing. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593,
2019.
14
Appendix
Table of Contents
A TL;DR dataset details 16
B Further model training details 17
B.1 Hyperparameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
B.2 Input format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
C Human data collection details 19
C.1 Process for ensuring high-quality human data . . . . . . . . . . . . . . . . . . . 19
C.2 Assessing human feedback quality . . . . . . . . . . . . . . . . . . . . . . . . 19
C.3 Labeler demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
C.4 Labeler website . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
C.5 Instructions for labelers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
C.6 Composition of the labeled dataset . . . . . . . . . . . . . . . . . . . . . . . . 22
C.7 Example comparison tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
D Choice of baselines 28
E CNN/DM lead-3 vs reference summaries 29
F Controlling for summary length 30
G Additional results 31
G.1 Value function ablation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
G.2 Evaluating policies along axes of quality . . . . . . . . . . . . . . . . . . . . . 31
G.3 Studying best-of-N optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 31
G.4 ROUGE scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
G.5 Bigram overlap statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
G.6 Reward model validation sets . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
G.7 Measuring agreement between different evaluation metrics . . . . . . . . . . . . 35
H Samples 38
H.1 Random samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
H.2 Overoptimized samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
15
A TL;DR dataset details
Here, we discuss the pre-processing steps that we apply
to the TL;DR dataset. We first remove all duplicate
Subreddit # posts % of dataset
posts by checking the text body, finding that there are relationships 63324 54.25%
nearly 20,000 exact duplicates. We then re-parse the AskReddit 15440 13.23%
TL;DR carefully using a set of heuristics, and filter to relationship_advice 8691 7.45%
use only top-level posts (rather than comments). We tifu 7685 6.58%
also filter out any post that is from a subreddit not in our dating_advice 2849 2.44%
‘subreddit whitelist’ (see Table 2 for the distribution personalfinance 2312 1.98%
over subreddits), any post where the title starts with Advice 2088 1.79%
some variant of ‘Edit’ or ‘Update’,10 and posts that legaladvice 1997 1.71%
contain certain topics (such as graphic sex or suicide) offmychest 1582 1.36%
using heuristics. Finally, to ensure the posts are short loseit 1452 1.24%
jobs 1084 0.93%
enough to fit into the context length of our models, we
self 1048 0.90%
filter out any post whose body is longer than 512 tokens. BreakUps 838 0.72%
This resulted in a set of 287,790 posts filtered by body askwomenadvice 688 0.59%
but not summary, of which we hold out approximately dogs 638 0.55%
5% as a validation set. We used this set of posts for running 567 0.49%
RL training since our RL procedure does not require pettyrevenge 548 0.47%
reference summaries. needadvice 528 0.45%
travel 452 0.39%
We next perform additional filtering on the parsed refer- Parenting 435 0.37%
ence summaries that we use for training our supervised weddingplanning 433 0.37%
baselines. Specifically, we remove summaries where Pets 366 0.31%
the TL;DR starts with variants of ‘Edit’, ‘Update’, or Dogtraining 362 0.31%
‘P.S.’, we heuristically remove summaries with certain cats 324 0.28%
levels of profanity, and we remove summaries that are AskDocs 283 0.24%
less than 24 tokens or more than 48 tokens. As dis- college 264 0.23%
cussed in Section 4.1, since our RL models tend to gen- GetMotivated 169 0.14%
erate summaries on the upper end of the allowed length books 161 0.14%
Cooking 114 0.10%
limit, this length filtering ensures that there is enough
length overlap between the RL summaries and refer- Table 2: Number of posts in the training
ence summaries for us to perform a length-controlled set of our filtered Reddit TL;DR dataset by
analysis. Additionally, we found that summaries shorter subreddit.
than 16 tokens were usually of low quality. We later
verified that the summaries we filtered out were lower
quality according to our reward model — more than 0.5 nats worse on average (i.e. they are predicted
to be exp(0.5) ≈ 1.6 times less likely to be preferred). Our final TL;DR dataset contains 123,169
posts including summaries, again with about 5% held out as a validation set. We use 1913 of these
validation articles for model selection during development; the evaluations in this paper exclude these
articles.
Note that, from Table 2 we can see that about two thirds of our TL;DR dataset consists of posts
relating to relationships or relationship advice, which is a fairly specific domain. This raises potential
concerns about the generality of our models, though their strong transfer performance on CNN/DM
news articles suggests they are not unreasonably specialized to relationship advice.
10
These posts are usually follow-ups of previous posts that have been posted to Reddit, and require the context
of the original post to fully understand.
16
Model size n_layers d_model n_heads Max LR Max batch size
1.3B 24 2048 16 2e-4 512
3B 32 2560 32 1.6e-4 512
6.7B 32 4096 32 1.2e-4 512
13B 40 5120 40 1e-4 1024
Table 3: Hyperparameters for our models of various sizes.
Figure 8: The sweep we conducted for determining our sampling procedure, varying the temperature
and the ‘top p’ value for nucleus sampling. While we didn’t do a large enough test to determine
whether nucleus sampling is better or worse than moderate-temperature sampling, we found that very
low temperature sampling is better than both on this task.
B Further model training details
B.1 Hyperparameters
All models follow the standard Transformer architecture, with 2048 learned position embeddings.
All models are trained with fp16 activations and the Adam optimizer [31]. Nearly all supervised
baselines, reward models, and reinforcement learning models are trained with fp32 weights; the
exception is our TL;DR supervised baselines, which were trained with fp16 weights.11 All models
are trained with the same byte-pair encoding as in [48].
During pretraining, the models were trained to predict the next token on a large text corpus consisting
of Commoncrawl, Webtext [48], books, and Wikipedia. Training lasts between 1-3 epochs on each,
for a total of 200-300 billion tokens. Learning rate follows a cosine schedule, with a short warmup,
decaying to 10% of the maximum value. The batch size ramped up throughout training to some
maximum, with each input having 2048 tokens. Hyperparameters for each model are shown in
Table 3.
For supervised baselines, we initialize models from the pretrained models. We decay the learning
rate with a cosine schedule, using an initial learning rate chosen from a log linear sweep of at least
7 values. This resulted in learning rates of 6.35e-5, 5.66e-5, 2.83e-5, and 2.83e-5 for our TL;DR
models of size 1.3B, 3B, 6.7B, and 13B respectively, and a learning rate of 2.38e-5 for our CNN/DM
6.7B model. We use a batch size of 128, and run for a single epoch.
For reward modeling, we initialize to the supervised baseline, but with a reward head on top with
weights initialized according to N (0, 1/(dmodel + 1)) [20]. We train for one epoch, decaying the
11
This was for a historical reason - we found that fp32 weights improved RL performance and so used it for all
our RL runs. This introduces a small discrepancy, since supervised runs trained in fp32 would have performed
slightly better. Unfortunately, we forgot to address this in our human evaluations. However, the effect on the
supervised loss corresponds to increasing model size by less than 20%, which is small compared to effect sizes
that are present in this paper (as seen in Figure 1.)
17
Trained models Format Max tokens
TL;DR (supervised, RL) SUBREDDIT: r/{subreddit}
TITLE: {title} 512
POST: {post}
TL;DR:
Transfer from TL;DR to {article} 512
CNN/DM (supervised, RL) TL;DR:
TL;DR (pretrained) {context_stuffed_with_examples}
=====
Subreddit: r/{subreddit} 1999
Title: {title}
{post}
TL;DR:
CNN/DM (supervised) Article: {article} 1999
TL;DR:
CNN/DM (pretrained) {context_stuffed_with_examples}
===== 1999
Article: {article}
TL;DR:
Table 4: Formats used for the context for each of our trained models on the TL;DR and CNN/DM
datasets.
learning rate with a cosine schedule, using an initial learning rate chosen from a log linear sweep
of at least 7 values. We also sweep over between 3 and 10 seeds, and choose the reward model
that performs best on the development portion of the validation set, as we find that both the data
iteration order and reward head initialization affect results [13]. For our main results, the 1.3B and
6.7B reward models had learning rates of 1.5e-5 and 5e-6, respectively. We use a batch size of 64,
and run for a single epoch.
For PPO, we run with separate policy and value networks, initializing our policies to the supervised
baseline, and our value functions to the reward model. We set γ = 1 and λ = 0.95 for the advantage
estimation [57] and do 4 epochs of optimization for each batch of rollouts. We used a linear learning
rate decay schedule, with initial learning rates of 1.5e-5 for the 1.3B model and 7e-6 for the 6.7B
model, based on small amounts of experimentation and rough model size extrapolation. We used a
KL coefficient of 0.05 for both of the main runs we report results for (except when we explicitly vary
this value in the reward model optimization graphs). We use a batch size of 512 for the 1.3B model
and 256 for the 6.7B model, and run for 1 million episodes.
B.2 Input format
Our model always receives a byte-pair encoded string of a fixed size. When the input is too small, we
pad from the beginning of the input with a padding token, and if the input is too long we truncate the
post/article field at newlines to stay under the limit.
When sampling from models pretrained only on our pretrain mixture and not fine-tuned on TL;DR,
we follow [48] and instead of padding with a padding token, we pad the beginning of the context
with examples of posts/articles and high-quality summaries. We use as many examples as will fit in
the token limit, with the examples formatted the same way as the main input. Table 4 documents the
formats we used (with pythonic format strings).
18
C Human data collection details
C.1 Process for ensuring high-quality human data
We first detail the procedures we use to ensure high-quality data. While these procedures became
more rigorous over the course of the project, they generally involved four steps.
Step 0: Understanding the task ourselves. To understand the task, we first do many summary
comparisons ourselves. We also hire a small number of human labelers12 to do comparisons, and
discuss our disagreements. We then draft instructions for a larger set of human labelers.
Step 1: Labeler onboarding. Labelers are hired from Upwork, a freelancing platform, as well as
two labeling services, Scale and Lionbridge. Labelers first complete a (paid) training process where
they label summaries on a shared set of data. For some comparisons, labelers get immediate feedback
about which summary was chosen by us, and why, to help them calibrate. We retain labelers that pass
a minimum threshold for speed and agreement with us. To allow for a customizable labeler interface,
we built our own website for data collection (see Appendix C.4).
Step 2: Collecting comparison data. Next, we have labelers evaluate a large batch of comparisons
on our website, which generates the bulk of our data. Before comparing two summaries directly, we
have labelers write their ‘naive interpretations’ of summaries without seeing the original post. We’ve
found this helpful for evaluating summaries, as they surface points of ambiguity in the summary
that might not have been detected if the summary was read after the original post. After doing naive
interpretations, labelers do comparisons by assigning a value on a 9-point scale for how confident
they are that summary A is better than summary B (or the converse).
Step 3: Providing labeler feedback. After collecting the comparison data, we can look at agreement
rates between labelers. While most comparisons are only given to a single labeler, each labeler gets
about 10-20% questions from a shared pool for calibration purposes. We can both attempt to use
these statistics as crude measures of quality, and show cases of disagreements to workers to help
them improve their labels.
Step 4: Researcher comparison calibrations. We occasionally also do the task ourselves, to
measure agreement rates between each labeler and us. This is used for quality assessment (see C.2).
We also calculate per-labeler "high confidence" thresholds, by finding the confidence value on the
Likert scale for each labeler such that we expect labels above this threshold to agree with us 80% of
the time on average. For the purposes of reward model selection, we filter the validation set to contain
only these higher confidence labels. For the entire process, we keep a high communication bandwidth
with labelers: we use a shared chat room for labelers to ask clarifying questions and discuss difficult
comparisons amongst themselves, host office hours, and occasionally have one-on-one video calls
with labelers to discuss points of disagreement.
We keep good labelers throughout the lifetime of the project, while firing the lowest-performing
workers.
C.2 Assessing human feedback quality
We assess labeler accuracy by comparing the labeler’s preferred summary with the summary we
prefer (ignoring the confidence level). We exclude comparisons where either the labeler or researcher
expresses indifference. This gives us an agreement rate, in theory ranging from 0% (perfect disagree-
ment) to 100% (perfect agreement). For our 2-way comparisons, a random labeler would get 50%
agreement.
To obtain our main number comparing labeler-researcher to researcher-researcher agreement, we
restrict ourselves to comparisons between summaries from our 1.3B supervised baseline, because this
subset of the data has the most researcher-labeled data. On this subset, labelers agree with researchers
77% ± 2% of the time, while researchers agree with each other 73% ± 4% of the time. We believe
substantial noise comes from comparisons being quite difficult and subjective.
In general, agreement rates range from about 65% for the least proficient labelers and most difficult
comparisons (comparing two high-temperature samples from a single RL policy) to about 85% for
12
We pay labelers an hourly wage, regardless of the number of comparisons completed.
19
(a) (b)
Figure 9: (a) The website we made to collect data from labelers. (b) Naive interpretations of
summaries on the website.
the most proficient labelers and easiest comparisons (comparing a high-temperature sample from
a supervised baseline to the reference summary). Averaging over all workers, weighted by their
volume, gives us an estimated agreement rate of 73% ± 3% for our reward model training corpus.
Labelers agree with each other 72% of the time in the training corpus. This suggests we could get
more reliable labels by aggregating labels from multiple workers on the same comparison. Indeed,
on the subset of the training data for which we have enough shared comparisons, taking the modal
label from 3 labelers increases their agreement rate with researchers from 72% to 77%. However, we
usually collect only one label per comparison, in order to maximize label throughput.
On the evaluations for Figure 1, labelers agreed with researchers 73% ± 3% of the time, and labelers
agreed with each other 73% ± 2% of the time.
Agreement rate between researchers ranged from about 65% on the most difficult comparisons
(comparing two high-temperature samples from a single RL policy), to about 80% on the easiest
comparisons (comparing a high-temperature sample from a supervised baseline to the human reference
summary), to about 95% in cases where we discussed the comparisons with each other.
Overall we believe that quality is fairly high. Our attempts to filter data generally hurt reward model
accuracy. For example, using the confidence thresholds mentioned above, we found that while
lower-confidence labels were less useful than high-confidence labels for improving reward model
accuracy, they were still better to include than to omit. Similarly, leaving out workers with poorer
agreement rates did not help.
C.3 Labeler demographics
When training machine learning models with human feedback, the humans providing the feedback
are essential in reinforcing the desired model behavior. If we are to scale human feedback to train
models on more complex tasks, where humans might disagree about what the desired model behavior
should be, it’s important for members of groups that will be impacted by the model to be included in
the labeler population.
To provide more transparency into our labeler demographics, we provide results from a survey given
to our labelers in Table 5. The survey was optional, anonymous, and it was made clear that the
results would not affect hiring or firing decisions. We find that our labelers span a range of ethnicities,
nationalities, ages, and genders, and educational backgrounds, but are more likely to be White and
American.
C.4 Labeler website
Since we hired and trained our own set of labelers, rather than using a crowdsourcing website such
as Amazon Mechanical Turk, we built our own website to allow for a standardized, customizable
user interface for all labelers. Each labeler created a separate profile, allowing us to assign different
sets of comparisons to different labelers. The website contains different renderers for different kinds
20
What gender do you identify as?
Male 38.1%
Female 61.9%
Nonbinary / other 0%
What ethnicities do you identify as?
White / Caucasian 42.9%
Southeast Asian 23.8%
Indigenous / Native American / 9.6%
Alaskan Native
East Asian 4.8%
Middle Eastern 4.8%
Latinx 4.8%
My ethnic identity isn’t listed 9.6%
What is your nationality?
American 45%
Filipino 30%
South African 5%
Serbian 5%
British 5%
Turkish 5%
Indian 5%
What is your age?
20-29 42.9%
30-39 23.8%
40-49 23.8%
50-59 9.5%
60+ 0%
What is your highest attained level of education?
Less than high school degree 0%
High school degree 14.3%
Undergraduate degree 57.1%
Master’s degree 23.3%
Doctorate degree 4.8%
Table 5: Demographic data from 21 of our labelers who participated in our voluntary survey.
of questions, including naive interpretations, summary comparisons, and Likert evaluations along
different axes, along with room for labelers to express concerns with the question or explanations for
their decision. Screenshots from the website are shown in Figure 9. Data collected from the website
can be easily ported into a central database containing all of our human data.
C.5 Instructions for labelers
Here we provide more detail on the specific instructions given to labelers for comparing summaries,
and for doing Likert evaluations of summaries along axes of quality. We produced separate sets
of instructions for evaluating Reddit posts, and for evaluating CNN/DM news articles. For Reddit
instructions, we first describe Reddit in general and provide a table that translates Reddit-specific
lingo into common parlance.
Instructions for comparing summaries. We show an excerpt of the instructions given to labelers
for making comparisons in Table 6. In addition to these instructions, we provide an example labeled
comparison between Reddit summaries, and also example naive interpretations for summaries.
Instructions for evaluating summaries along axes of quality. We provide a separate set of de-
tailed instructions for labelers for the 7-point Likert evaluations. We first introduce each of the 4 axes
of quality we consider, giving an overview of coherence, accuracy, coverage, and overall score (shown
in Table 7). We also provide a brief rubric for giving scores of 1, 4, and 7, along with several Reddit
summaries annotated with our own judgments of quality along each of these axes (with explanations).
21
What makes for a good summary? Roughly speaking, a good summary is a shorter piece of text
that has the essence of the original – tries to accomplish the same purpose and conveys the same
information as the original post. We would like you to consider these different dimensions of
summaries:
Essence: is the summary a good representation of the post?
Clarity: is the summary reader-friendly? Does it express ideas clearly?
Accuracy: does the summary contain the same information as the longer post?
Purpose: does the summary serve the same purpose as the original post?
Concise: is the summary short and to-the-point?
Style: is the summary written in the same style as the original post?
Generally speaking, we give higher weight to the dimensions at the top of the list. Things are
complicated though – none of these dimensions are simple yes/no matters, and there aren’t hard
and fast rules for trading off different dimensions. This is something you’ll pick up through
practice and feedback on our website.
Table 6: An excerpt from the instructions we gave to labelers for doing comparisons.
Finally, we provide a FAQ section that answers common questions raised by the small initial set of
labelers we assigned to this task.
For CNN/DM, we provide the same set of instructions, except we add some additional clarifications
for how to judge news articles. We specifically ask labelers to place less emphasis on fluidity of
sentences (because the reference summaries were originally written in bullet-point form, and we
didn’t want labelers to penalize this), and to place less emphasis on the summary matching the intent
of the article (which was important for Reddit summaries).
In terms of quality control, we conducted a smaller version of the quality control process described
in Appendix C.1: we first labeled a small set of summaries ourselves along each axis to understand
points of confusion, then we wrote the instructions document to provide to labelers, then we had a
small number of labelers do a trial of the task to catch any remaining bugs or points of confusion, and
finally we onboarded a larger set of labelers onto the task while remaining available to answer any
questions.
C.6 Composition of the labeled dataset
Over the course of the project, we trained several reward models and policies. Each batch of
summaries that we sent to the labelers were sampled from a variety of policies. We didn’t have a
systematic plan for which policies to sample from; rather, we chose what seemed best at the time in
the spirit of exploratory research. Every time we trained a reward model, we trained on all labels we
had collected so far. Successive models also benefited from improved hyperparameters and dataset
cleaning. Our results could likely be replicated with a simpler, more systematic approach.
In general, as we hire new labelers and as existing labelers perform the task more, it is possible that
there is ‘labeler drift’, where the set of criteria used by labelers to evaluate summaries gradually shifts
over time. This could lead to a regression in labeler-researcher disagreement, or lead to some policies
becoming more or less preferred over time. To help guard against this, in most batches we include
comparisons between samples from our supervised baseline and reference summaries, and measure
the frequency with which the workers prefer one over the other. If this number drifts over time, it’s
an indication that our workers’ preferences are also changing. However, we generally found that this
preference number stayed relatively constant, within noise.
Table 8 lists the policies we trained by supervised finetuning on the TL;DR dataset, as well as the
reward models, trained on successively larger datasets of human labels. Table 9 lists the RL policies.
22
Coherence
For this axis, answer the question “how coherent is the summary on its own?” A summary is
coherent if, when read by itself, it’s easy to understand and free of English errors. A summary is
not coherent if it’s difficult to understand what the summary is trying to say. Generally, it’s more
important that the summary is understandable than it being free of grammar errors.
Rubric:
Score of 1: The summary is impossible to understand.
Score of 4: The summary has mistakes or confusing phrasing that make it a bit hard to understand.
Score of 7: The summary is perfectly clear.
Accuracy
For this axis, answer the question “does the factual information in the summary accurately match
the post?” A summary is accurate if it doesn’t say things that aren’t in the article, it doesn’t mix up
people, and generally is not misleading. If the summary says anything at all that is not mentioned
in the post or contradicts something in the post, it should be given a maximum score of 5. (If you
are confused about how to use ‘6’, see the FAQ!)
Rubric:
Score of 1: The summary is completely wrong, made up, or exactly contradicts what is written in
the post.
Score of 4: The summary says at least one substantial thing that is not mentioned in the post, or
that contradicts something in the post.
(Score of 5: The summary says anything, no matter how small, that is not mentioned in the post,
or that contradicts something in the post.)
Score of 7: The summary has no incorrect statements or misleading implications.
Coverage
For this axis, answer the question “how well does the summary cover the important information
in the post?” A summary has good coverage if it mentions the main information from the post
that’s important to understand the situation described in the post. A summary has poor coverage if
someone reading only the summary would be missing several important pieces of information
about the situation in the post. A summary with good coverage should also match the purpose of
the original post (e.g. to ask for advice).
Rubric:
Score of 1: The summary contains no information relevant to the post.
Score of 4: The summary is missing at least 1 important piece of information required to under-
stand the situation.
Score of 7: The summary covers all of the important information required to understand the
situation.
Overall quality
For this axis, answer the question “how good is the summary overall at representing the post?”
This can encompass all of the above axes of quality, as well as others you feel are important. If
it’s hard to find ways to make the summary better, give the summary a high score. If there are lots
of different ways the summary can be made better, give the summary a low score.
Rubric:
Score of 1: The summary is terrible.
Score of 4: The summary is an okay representation of the post, but could be significantly improved.
Score of 7: The summary is an excellent representation of the post.
Table 7: Instructions given to labelers for evaluating summaries along four different axes of quality.
23
Supervised policy name # Parameters Reward model name # Parameters
sup1 750M rm1 1.3B
sup2 1.3B rm2 6.7B
sup3 1.3B rm3 1.3B
sup3_6b 6.7B rm3_6b 6.7B
sup4 1.3B rm4 1.3B
sup4_6b 6.7B rm4_6b 6.7B
Table 8: Left: supervised baselines. sup4 and sup4_6b are the final supervised baselines used
throughout the paper. Right: reward models. rm4 and rm4_6b are the final reward models used
throughout the paper.
RL policy name # Parameters Objective Initialization KL coefficient KL(ppo, sup)
sup3 ppo rm1 1.3B rm1 sup3 0.35 1.8
sup4 ppo rm3 1 1.3B rm3 sup4 0.10 3.8
sup4 ppo rm3 2 1.3B rm3 sup4 0.07 9.4
sup4 ppo rm3 3 1.3B rm3 sup4 0.05 19.0
sup4 ppo rm4 1.3B rm4 sup4 0.05 18.0
sup4_6b ppo rm4_6b 6.7B rm4_6b sup4_6b 0.05 14.0
Table 9: PPO policies. sup4 ppo rm4 and sup4_6b ppo rm4_6b are the final policies used throughout
the paper.
BoN policy name Objective Base policy N KL(BoN, sup)
sup2 bo8 rm1 rm1 sup2 8 1.2
sup3 bo8 rm1 rm2 sup3 8 1.2
sup3 bo63 rm2 rm2 sup3 63 3.2
sup4 bo8 rm3 rm3 sup4 8 1.2
sup4 bo64 rm3 rm3 sup4 64 3.2
sup4 bo128 rm3 rm3 sup4 128 3.9
sup4 bo256 rm3 rm3 sup4 256 4.5
sup4 bo512 rm3 rm3 sup4 512 5.2
sup4 bo128 rm3_6b rm3_6b sup4 128 3.9
sup4 bo256 rm3_6b rm3_6b sup4 256 4.5
Table 10: Best-of-N policies. KL divergence is computed analytically as KL(boN, sup) = log N -
(N-1)/N.
We also explored a simple alternative to reinforcement learning: Sample N summaries from a
supervised baseline at temperature 0.7, score them with a reward model, and take the summary with
the highest score. This best-of-N (BoN) procedure is effectively a mildly optimized policy requiring
no training. These policies are named in Table 10, and samples from them form part of the training
data.
Table 11 lists the source policies for the training data for each reward model.
Label count
Reward model Policy0 Policy1
rm1 ref sup1 5404
sup1 sup1 5386
rm2 ref sup1 5404
sup2 12779
sup2 bo8 rm1 1426
sup3_6b 1424
sup1 sup1 5386
Continued on next page
24
Label count
Reward model Policy0 Policy1
sup2 sup2 11346
sup2 bo8 rm1 1376
sup3_6b 1383
sup2 bo8 rm1 sup3_6b 1390
rm3, rm3_6b ref sup1 5404
sup2 12779
sup2 bo8 rm1 1426
sup3 438
sup3 bo63 rm2 447
sup3 bo8 rm2 887
sup3 ppo rm1 884
sup3_6b 1424
sup1 sup1 5386
sup2 sup2 11346
sup2 bo8 rm1 1376
sup3_6b 1383
sup2 bo8 rm1 sup3_6b 1390
sup3 sup3 bo8 rm2 428
sup3 ppo rm1 416
sup3 bo63 rm2 sup3 bo8 rm2 432
sup3 ppo rm1 444
sup3 bo8 rm2 sup3 ppo rm1 855
rm4, rm4_6b ref sup1 5404
sup2 12779
sup2 bo8 rm1 1426
sup3 438
sup3 bo63 rm2 447
sup3 bo8 rm2 887
sup3 ppo rm1 884
sup3_6b 1424
sup4 1335
sup4 bo128 rm3 602
sup4 bo128 rm3_6b 203
sup4 bo256 rm3 307
sup4 bo256 rm3_6b 101
sup4 bo512 rm3 52
sup4 bo64 rm3 52
sup4 bo8 rm3 393
sup4 ppo rm3 1 981
sup4 ppo rm3 2 215
sup4 ppo rm3 3 208
sup4_6b 104
sup1 sup1 5386
sup2 sup2 11346
sup2 bo8 rm1 1376
sup3_6b 1383
sup2 bo8 rm1 sup3_6b 1390
sup3 sup3 bo8 rm2 428
sup3 ppo rm1 416
sup3 bo63 rm2 sup3 bo8 rm2 432
sup3 ppo rm1 444
sup3 bo8 rm2 sup3 ppo rm1 855
sup4 sup4 1051
sup4 ppo rm3 1 395
Continued on next page
25
Label count
Reward model Policy0 Policy1
sup4 bo128 rm3 sup4 bo128 rm3 288
sup4 bo256 rm3 582
sup4 bo128 rm3_6b sup4 bo128 rm3_6b 95
sup4 bo256 rm3_6b 203
sup4 bo512 rm3 sup4 ppo rm3 3 216
sup4_6b 60
sup4 bo64 rm3 sup4 ppo rm3 2 218
sup4_6b 55
sup4 bo8 rm3 sup4 ppo rm3 1 752
sup4 ppo rm3 1 sup4 ppo rm3 1 372
sup4 ppo rm3 2 sup4 ppo rm3 2 4256
sup4_6b 215
sup4 ppo rm3 3 sup4 ppo rm3 3 4037
sup4_6b 216
Table 11: Training data for reward models. "ref" refers to human reference summaries.
C.7 Example comparison tasks
To give a sense of the difficulty of the comparisons task, we provide example comparisons between
two summaries generated by our 6.7B human feedback model. In Table 12 we show both a random
comparison drawn from the TL;DR dataset, and a cherry-picked comparison (selected from 10
comparisons where labelers disagreed) to illustrate the trade-off between accuracy in coverage that
can occur when labelers conduct evaluations.
26
Random TL;DR comparison
POST
Subreddit: r/Pets
TITLE: What do you use for flea control?
My family has tried literally EVERYTHING to control the fleas in our neighborhood (Frontline, Advantage,
Diatomaceous Earth, Dawn Dishsoap, etc!) and nothing has worked. I have spoken to lots of pet owners in
my area (I work as a vet assistant) and many are reporting similar results, where fleas are becoming resistant
to the usually recommended treatments. The only thing that has worked so far is Comfortis, but I’ve read of
several dogs having reactions to it that can be pretty severe. My dogs are fine, we’ve used it for about a year
now, but I don’t like the idea of harming them or putting them at risk.
Giving them baths with blue Dawn dish soap does kill all the fleas, but it does nothing to prevent more from
coming back, obviously. It only kills on contact, and we are NOT going to over bath them because that isn’t
healthy either. We’re looking for something that lasts.
Does anyone else have experience with this, or any detailed information on Comfortis and if it does serious
damage to your pet’s system? Yes, I know I am a vet assistant. My boss strictly recommends Frontline and
literally will not listen to me when I tell him it doesn’t work and my dogs are still covered in fleas and we
have to use Comfortis because it is the only thing that gives them relief. He is not a resource in this case.
Just wanted to see what other pet owners (specifically ones in San Diego) do for fleas...the ones we have here
are mutants or something, because almost nothing works on them!
Summary A: Fleas are developing resistance to most flea control products (including Comfortis). Looking
for something that lasts long term that doesn’t harm my dogs. Does anyone have experience with any of the
listed products?
Summary B: Nothing has worked on our fleas, we are looking for something that lasts, Comfortis is not a
long term solution. Does anyone else have experience with flea control or have information on Comfortis?
Hard TL;DR comparison
POST
Subreddit: r/weddingplanning
TITLE: Feeling major anxiety about dress shopping.
So, not really sure if I’m asking for advice or just a small rant. We got engaged March 2, 2013. From day 1
we’ve been struggling through the planning. At first, it was arguing with his parents about us getting married
in a church. And then it was an argument about which venue to have the reception. We finally have the venue
booked and the church matter settled. Now that’s out of the way, I suddenly have this pit in my stomach
My mom left me when I was 14. I’ve basically done everything on my own and I have really been ok about it.
I’m sure it’s not of the norm for me to feel so disassociated about the whole thing, but I am. I’m suppose to
go look at wedding dresses this Friday. I am feeling super anxious because I don’t know if trying on wedding
dresses is going to turn me into a blubbering baby about not having a mom.
My future mother-in-law is suppose to come with me to help look. I worry about turning into that blubbering
baby and offending her. I don’t want her thinking that I don’t appreciate her being there.
Aside from me worrying about becoming a giant baby, I’ve also been having issues with my bridal party.
While I haven’t made any official choices, I have ideas of who I want involved. That would be my best
friend, my sister, and my future sister-in-law. My first choice for a MOH is my best friend. However, she
lives out of state, and is in a medical program for school. So her visit time is severely limited. My sister feels
entitled to be the MOH, despite the fact that we are not close at all. So getting people together to get any
kind of wedding stuff done is almost impossible.
Summary A: I’m having doubts about whether or not to try on wedding dresses. I am also having doubts
about my bridal party’s ability to get things done.
Summary B: I think I’m going to turn into a blubbering baby and offend my mother-in-law.
Table 12: Top: Example of a random comparison task on the TL;DR dataset between two summaries
from our 6.7B human feedback model. Comparison chosen randomly from the validation set. Bottom:
An example of a difficult comparison task on the TL;DR dataset. Chosen by looking at comparisons
between supervised baseline summaries with at least 4 labeler judgements and with at least 40% vote
for each summary. Cherry-picked out of 10 to highlight an accuracy-coverage tradeoff. Summary A
is inaccurate since the author does not explicitly say she is having doubts about trying on wedding
dresses. Summary B is entirely accurate but does not capture the general essence of the post. In this
case, 4 workers chose A and 3 workers chose B. For more comparisons, see our website.
27
D Choice of baselines
In testing our human feedback techniques, we collected a large amount of high-quality data from
human labelers. In order to compare fairly against supervision-based techniques, we would have
needed to spend a similar amount of labeler time collecting high quality demonstrations, and used
those to fine-tune a model via supervised learning. Because this is prohibitively expensive, we do not
provide such a baseline.
Existing prior work such as PEGASUS [70] has studied supervised methods on a dataset very similar
to ours (the /r/tifu subset of TL;DR). However, they use much smaller (500M parameters) models,
and report that their model outputs are worse than the human reference summaries, according to
human evaluations. Thus, due to our limited labeler budget for evaluation, we decided to use our own
supervised and zero-shot models as baselines (after sanity-checking the ROUGE performance of our
supervised models), as well as T5 [49].
T5 models [49] are pretrained and fine-tuned in a similar way to our supervised baselines, but they
use an encoder-decoder architecture. We used T5 outputs which were obtained via beam search
decoding, as described in [49]. We also carefully account for differences in tokenization between
model outputs.13
13
Since tokenization affects capitalization and punctuation of the model outputs, we normalized all CNN/Daily
Mail outputs from all models by lower-casing everything and then heuristically re-capitalizing. We verify that
this normalization procedure produces identical results for reference summaries tokenized in different ways.
28
E CNN/DM lead-3 vs reference summaries
On the CNN/DM dataset, our labelers significantly preferred lead-3 (a summary consisting of the first
3 sentences of the article) to reference summaries. In part this is due to longer summaries receiving
higher coverage scores and lead-3 being 50% longer, as shown in Table 13.
Policy Length (stdev) Quality
Quality increase
/ 100 char.
ref 314 (119) 5.54 0.14
lead-3 475 (114) 6.23 0.34
Table 13: How length affects overall quality on CNN/DM for lead-3 and reference summaries.
However, if we use a linear regression (similar to the procedure in Appendix F) to predict what lead-3
performance would be if its average length were reduced to 314 characters, we still find a quality
of 5.68, modestly higher than the reference summaries. Moreover, for lead-3 to even achieve parity
with the reference summaries seems to call into question the need for abstractive summarization or
sophisticated ML methods, since a simple extractive baseline can match a perfect imitation of the
reference summaries.
We wanted to understand labeler behavior on these comparisons, to ensure that it was not an error.
To do this, we examined a sample of our labeler’s judgments ourselves. We found that in 20/143
cases labelers preferred lead-3 by 3 points or more, and that excluding these datapoints would raise
the relative score of the reference summaries by about 0.5 points.14 We were surprised to see the
reference summaries performing so poorly in a significant fraction of cases, so we looked at labeler’s
explanations and confirmed they made sense.
We found that two features of the reference summaries explained most of its underperformance. First,
13 of these 20 summaries omitted one of the key points from the article—the highlights are often
written for a reader who had already seen the title of the article, even though the titles are not included
in the CNN/DM dataset. Second, 10 of these 20 summaries actually introduced new information not
present in the original article. From the perspective of labelers this information is totally confabulated
and so led to lower scores. A likely explanation for these errors is that the reference summaries are
extracted from “highlights” on the news sites rather than being a straightforward summary of the
article. These failures are common enough that they significantly impact the average quality of the
reference summaries, and the effects seem to be large relative to quality differences between ML
models.
Overall we believe that labeler judgments were reasonable in these cases, and that it is potentially
problematic to treat the “highlights” in the CNN/DM dataset as reference summaries. You can view
all of our labeler’s judgments on CNN/DM at our website.
14
The reference summaries were preferred to lead-3 by a similar margin in only 7/143 cases.
29
Human feedback*
Human feedback
Reference summaries
Supervised learning
Supervised
learning*
Pretrain only
Pretrain only* *: length-controlled
(a) (b)
Figure 10: (a) A length-controlled version of Figure 1, using the procedure described in Appendix
F. Controlling for length reduces the relative preference of our human feedback models, however
they are still preferred to the reference summaries. (b) Plotting model quality for different summary
lengths on the TL;DR dataset. Our 6.7B human feedback model outperforms both the 6.7B supervised
baseline and the reference summaries (horizontal line at 0.5) across lengths.
F Controlling for summary length
As discussed in Section 4.1, the length of a summary is a confounding factor for evaluating summary
quality; depending on the desired trade-off between conciseness and coverage, a shorter or longer
summary might be better. Our models generate summaries that are longer than the reference
summaries, as this led to higher labeler preference given the 24-48 token limit for our task. Here we
describe the procedure we use to attempt to control for length.
To calculate a single length-controlled preference number, we train a logistic regression model to
predict the human-preferred summary on our dataset of human comparisons. We provide this model
with 2 features: the identity of each policy, and the log ratio of the summary lengths. To calculate
the length-controlled preference value between two policies, we simply give each policy ID to our
trained logistic regression model and set the log length ratio to zero (see Figure 10a). In Figure 10b
we examine summary quality across a range of summary lengths on TL;DR. We find that our human
feedback model outperforms the supervised baseline across all length values.
For CNN/DM, we use a similar procedure as described above to control for length, except using a
linear regression model to predict the Likert rating from 1-7. We show the expected quality increase
for making summaries 100 characters longer in Table 14, which suggests our human feedback models
would perform better if they generated longer summaries.
Policy Length Quality Quality %
(stdev) (1-7) / 100 char.
sl(tldr)-1.3b 138 (34) 4.26 0.68
sl(tldr)-6.7b 127 (31) 4.41 0.38
gpt-1.3b 141 (41) 4.11 0.63
gpt-6.7b 142 (36) 4.6 0.3
rl(tldr)-1.3b 166 (30) 4.86 1.28
rl(tldr)-6.7b 175 (30) 5.25 0.87
sl(cnn)-6.7b 300 (103) 5.4 0.37
ref 314 (119) 5.54 0.14
lead-3 475 (114) 6.23 0.34
T5 316 (95) 5.92 0.3
Table 14: How length affects overall quality on CNN/DM. We show average length and quality
scores for various policies, and how much the summary quality increases on average per 100 added
characters.
30
G Additional results
G.1 Value function ablation
In this section, we conduct an ablation comparing using separate parameters for the value function
and policy, against using a shared network as done in [73]. The results, shown in Figure 11, clearly
indicate that using separate networks outperforms the latter. On the other hand, having separate
networks increases the memory requirements of running RL fine-tuning. Having separate networks
also allows us to initialize the value function to be the learned reward model that is being optimized.
Figure 11: Comparing the reward obtained by optimizing with separate value function and reward
model parameters to shared parameters.
G.2 Evaluating policies along axes of quality
We show the full results of the evaluations of policies on a 7-point Likert scale along different axes
of quality; for TL;DR this is shown in Figure 12, and for CNN/DM this is shown in Figure 13. It is
evident that on both datasets coverage correlates strongly with overall score across models, and all
models achieve a high coherence score.
G.3 Studying best-of-N optimization
A natural way to evaluate an automatic evaluation metric is to see the extent to which optimizing
against it leads to high performance according to humans. One way to assess this is to use best-of-N
as an (inefficient) optimization technique — this has the benefits of being simple and invariant to
monotonic transformations. We report results for up to best-of-2048 on ROUGE and three of our
reward models in Figure 7, using samples from the 1.3B supervised baseline. The results suggest
that optimizing against ROUGE significantly under-performs optimizing against our reward models.
The data also suggests ROUGE degrades with too much optimization much faster than our reward
models.
With increasing N, the best-of-N policies get higher average reward. Similarly, by decreasing the
KL coefficient β, the PPO policies get higher average reward. We found that at a given average
reward, the best-of-N and PPO policies have similar quality as judged by human labelers (not shown).
However, the PPO policy is farther from the supervised baseline than best-of-N is, as measured by
the KL divergence.15
G.4 ROUGE scores
In Figure 14a and 14b, we show the ROUGE scores of our models on the TL;DR and CNN/DM
datasets, respectively. We report results with T=0, consistent with our human evaluations. We found
that temperature has an (often significant) impact on ROUGE score, and we did a thorough sweep to
verify that the best temperature setting is T=0.
15
We can use KL from the supervised baseline as a distance metric. Note that we can calculate the KL of a
best-of-N policy analytically as log(n) − n−1
n
.
31
Figure 12: Evaluating TL;DR policies on a 7-point Likert scale along several axes of quality.
Model ROUGE-1 ROUGE-2 ROUGE-L
ProphetNet [67] 44.20 21.17 40.69
T5 [49] 43.52 21.55 40.69
Our 6.7B supervised model 42.49 19.84 39.53
CNN-2sent-hieco-RBM [71] 42.04 19.77 39.42
Table 15: Comparing the ROUGE score of our 6.7B supervised model on CNN/DM to recent SOTA
models from the literature. Without any summarization-specific engineering, our model achieves
ROUGE scores better than SOTA models from mid-2019, indicating that it is a strong baseline for
comparison.
32
Figure 13: Evaluating CNN/DM policies on a 7-point Likert scale along several axes of quality.
On TL;DR, we find that our human feedback models obtain a slightly lower ROUGE score than
the supervised models at T = 0, further indicating that ROUGE correlates poorly with human
preferences. For supervised models, lowering temperature has a larger impact than increasing model
size. Interestingly, at higher temperatures, our feedback models actually outperform supervised
counterparts (not shown).
On CNN/DM, ROUGE agrees with our human evaluations that our human feedback models transfer
better than our supervised models. However, unsurprisingly, supervised CNN/DM models still
achieve much higher ROUGE. In Table 15, we show the ROUGE results on CNN/DM for our 6.7B
supervised baseline and various models from the literature. We find that our model achieves ROUGE
scores less than T5 [49], but slightly greater than the CNN-2sent-hieco-RBM model from [71], which
was SOTA for abstractive summarization on CNN/DM in mid-2019 according to the NLP-progress
leaderboard.16
G.5 Bigram overlap statistics
In Table 16, we show the bigram overlap statistics for our models on the TL;DR and CNN/DM
datasets as a proxy for how much the summaries copy frmo the post. As in Section 4.4, we compute
the longest common subsequence of bigrams with the original Reddit post or news article, and
dividing by the number of bigrams in the summary. We find that models evaluated on CNN/DM
16
http://nlpprogress.com/english/summarization.html
33
(a) (b)
Figure 14: ROUGE scores for our models on (a) the TL;DR dataset, and (b) the CNN/DM dataset.
Evaluated on TL;DR
Model Model size Bigram overlap %
GPT 1.3B 66.7%
GPT 3B 72.7%
GPT 6.7B 61.4%
GPT 13B 75.9%
Supervised (TL;DR) 1.3B 49.0%
Supervised (TL;DR) 3B 48.7%
Supervised (TL;DR) 6.7B 48.9%
Supervised (TL;DR) 13B 48.0%
Human feedback (TL;DR) 1.3B 53.3%
Human feedback (TL;DR) 6.7B 46.0%
Evaluated on CNN/DM
Model Model size Bigram overlap %
GPT 1.3B 76.3%
GPT 6.7B 76.2%
Supervised (TL;DR) 1.3B 59.5%
Supervised (TL;DR) 6.7B 56.9%
Human feedback (TL;DR) 1.3B 64.8%
Human feedback (TL;DR) 6.7B 51.2%
Supervised (CNN/DM) 1.3B 66.0%
T5 11B 68.8%
reference — 36.8%
Table 16: Bigram overlap statistics on the TL;DR dataset (top) and the CNN/DM dataset (bottom).
Models trained on CNN/DM copy significantly more than models trained on TL;DR.
(whether or not they were trained on CNN/DM) generally copy more than models evaluated on
TL;DR. Further, our supervised and human feedback models copy less than our pretrained models.
G.6 Reward model validation sets
In this section, we report results evaluating our reward models on various manually constructed
validation sets, shown in Tables 17 and 18. Notably, we asked our humans to produce a small dataset
of edits, by having them make improvements to existing summaries (either reference summaries or
supervised baseline summaries). Our 6.7B reward model prefer the improved summaries at a similar
rate to humans (who do not know which summary has been edited).
Our reward models are also sensitive to sentence shuffling (whereas metrics like ROUGE are largely
not), and are able to detect when the roles portrayed in the summary have been switched. On the
other hand, our reward models sometimes exhibit preference for poor artificial summaries, such as
34
RM size Edit length RM prefers edit Human prefers edit RM, human agree
Shorter 63.6% 76.2% 62.1%
1.3B Longer 86.8% 88.6% 79.6%
Avg. 81.2% 85.6% 75.4%
Shorter 66.0% 76.2% 65.5%
6.7B Longer 89.2% 88.6% 80.2%
Avg. 83.7% 85.6% 76.7%
Table 17: Comparing reward model and human preference of summaries that were edited by humans
to make them better. For each summary, the human labeler that makes the comparison is different
than the labeler that wrote the edit. The agreement numbers do not include comparisons where the
labeler’s preference was marked as ‘uncertain’.
Preference % of Summary A
Summary A Summary B 1.3B RM 6.7B RM
Original summary Reversed roles 93.1% 97.4%
lead-3 Shuffled lead-3 68.1% 75.5%
rand-3 Shuffled rand-3 60.8% 76.1%
Post title Random title 97.4% 98.5%
Post title Random title from same subreddit 98.8% 97.2%
Post title Post title repeated twice 84.6% 58.4%
(r/tifu only) Reference summary Ref + “What should I do?” 34.3 % 74.5%
Reference summary lead-3 63.0% 56.4%
Reference summary lead-2 71.0% 73.8%
Reference summary rand-3 69.5% 59.5%
Table 18: Reward model performance on various manually constructed validation sets. In all cases,
Summary A is intended to be better than Summary B, and thus a higher preference % is generally
better. ‘rand-3‘ indicates a baseline where 3 random sentences are taken from the post; however these
sentences are kept in the order in which they appear in the post. ‘Original summary’ is either the
reference summary or a summary from our supervised baselines. r/tifu is a subreddit whose purpose
is sharing embarrassing stories (not asking for advice).
the post title copied twice, or asking for advice at the end of the summary. In Table 19, we show
examples where our model is sensitive to small, semantically meaningful changes in the summary.
G.7 Measuring agreement between different evaluation metrics
We are interested in understanding the relationship between different metrics for evaluating summaries.
To do this, we compute agreement between various metrics, including automatic metrics and humans,
for different subsets of the data for which we have human evaluations. To remove policy quality
as a confounding variable, all of the summary comparisons are generated by the same policy at the
same temperature value. In Table 20, we use samples from our 1.3B supervised model at T=0.7 on
TL;DR; Table 21 has comparisons from our 6.7B supervised model at T=0.7 on TL;DR; Table 22
has comparisons from our 6.7B human feedback model at T=0.7 on TL;DR; and Table 23 has
comparisons from our 6.7B supervised baseline trained on CNN/DM.
Our 6.7B reward model generally agrees with labelers as much as other labelers, although an
ensemble of labelers does better. On the other hand, ROUGE generally has poor agreement, as does
log probability under the supervised baselines, with simple heuristics like copying (longest common
subsequence of bigrams with the article) and length often performing comparably.
35
Edited summary Reward ∆
Crush on girl I haven’t seen in 4 years. She doesn’t like me and I don’t still like +0.64
her. What do?
A girl told me she loved liked me, she ended up picking another guy over me, +0.82
that guy badly influenced her, and now I’m here alone thinking what could’ve
been.
I tried to show my friend a picture of my tarantula and she smashed my phone -0.64
with all her might and now I lost a good friend phone.
Boyfriend still FB stalks his high school ex girlfriend from time to time and +0.73
told me when he was very drunk that she was his first love.
I’ve become pathetic, pining after a guy my ex. Would like to reach state of less +0.69
pathetic. If more info is necessary, please let me know.
I have body issues (body acne/scarring and weight issues) that prevent me from +1.0
having a normal life without shame and prevent me from having a better sex
life with my BF.
Do you take someone back after they’ve turned you down off, even if you can’t +0.52
see them in person or are they just not worth the risk?
Table 19: Qualitative examples showing the change in reward of the reward model on human-
generated edits to TL;DR summaries that make the summaries better. Examples are randomly
selected from the set where the edit distance was less than 5 and the magnitude of change in reward
was greater than 0.5. Text in strike-through was removed from the original summary in the edit, and
text in bold was added. The reward model is sensitive to small but semantically meaningful changes
in the summary, although it makes errors on occasion.
TL;DR
1.3B sup. researcher labeler labeler length copying ROUGE 1.3B 1.3B 6.7B 6.7B
T=0.7 ensem- sup RM sup RM
ble logprob logprob
researcher 73.4% 77.7% 84.4% 55.5% 62.3% 59.1% 61.8% 72.2% 62.8% 78.0%
±4.1% ±2.1% ±3.3% ±4.3% ±4.1% ±4.2% ±4.8% ±4.5% ±4.7% ±3.9%
labeler 77.7% 68.6% 74.4% 54.4% 58.0% 57.7% 58.7% 65.8% 61.9% 70.8%
±2.1% ±1.7% ±2.0% ±1.3% ±1.2% ±1.3% ±2.0% ±2.0% ±2.1% ±1.8%
labeler
84.4% 74.4% — 60.6% 62.7% 59.0% 59.5% 71.0% 59.5% 72.5%
ensemble
±3.3% ±2.0% ±4.0% ±3.8% ±3.9% ±4.4% ±3.9% ±4.3% ±3.8%
length 55.5% 54.4% 60.6% — 50.1% 58.6% 28.9% 52.6% 27.6% 54.3%
±4.3% ±1.3% ±4.0% ±1.3% ±1.2% ±2.1% ±2.3% ±2.0% ±2.3%
copying 62.3% 58.0% 62.7% 50.1% — 51.9% 61.6% 57.8% 60.9% 55.5%
±4.1% ±1.2% ±3.8% ±1.3% ±1.2% ±2.3% ±2.3% ±2.2% ±2.2%
ROUGE 59.1% 57.7% 59.0% 58.6% 51.9% — 49.5% 56.4% 51.1% 59.2%
±4.2% ±1.3% ±3.9% ±1.2% ±1.2% ±2.3% ±2.2% ±2.3% ±2.3%
1.3B sup.
61.8% 58.7% 59.5% 28.9% 61.6% 49.5% — 58.7% 92.7% 60.6%
logprob
±4.8% ±2.0% ±4.4% ±2.1% ±2.3% ±2.3% ±2.3% ±1.2% ±2.3%
1.3B RM 72.2% 65.8% 71.0% 52.6% 57.8% 56.4% 58.7% — 58.8% 78.8%
±4.5% ±2.0% ±3.9% ±2.3% ±2.3% ±2.2% ±2.3% ±2.2% ±1.8%
6.7B sup.
62.8% 61.9% 59.5% 27.6% 60.9% 51.1% 92.7% 58.8% — 61.5%
logprob
±4.7% ±2.1% ±4.3% ±2.0% ±2.2% ±2.3% ±1.2% ±2.2% ±2.2%
6.7B RM 78.0% 70.8% 72.5% 54.3% 55.5% 59.2% 60.6% 78.8% 61.5% —
±3.9% ±1.8% ±3.8% ±2.3% ±2.2% ±2.3% ±2.3% ±1.8% ±2.2%
Table 20: Agreement rates between humans and various automated metrics on TL;DR 1.3b supervised
model at T=0.7. Standard errors estimated via bootstrapping. Note: in the entry for labeler vs. labeler
ensemble, the ensembles are slightly smaller than for other comparisons because we need to exclude
the labeler being predicted. All ensembles have at least 3 workers.
36
TL;DR
6.7B sup. labeler labeler length copying ROUGE 1.3B 1.3B 6.7B 6.7B
T=0.7 ensem- sup RM sup RM
ble logprob logprob
labeler 70.8% 73.1% 56.9% 56.4% 56.9% 54.5% 67.5% 54.3% 69.7%
±2.6% ±2.9% ±0.6% ±0.6% ±0.6% ±1.2% ±1.1% ±1.2% ±1.1%
labeler
73.1% — 55.0% 54.5% 66.7% 61.1% 77.8% 55.6% 77.8%
ensemble
±2.9% ±5.1% ±4.8% ±4.7% ±11.4% ±9.7% ±11.7% ±10.0%
length 56.9% 55.0% — 50.5% 60.2% 26.9% 59.5% 26.4% 60.3%
±0.6% ±5.1% ±0.6% ±0.6% ±1.1% ±1.2% ±1.1% ±1.1%
copying 56.4% 54.5% 50.5% — 54.4% 59.3% 57.9% 60.2% 58.0%
±0.6% ±4.8% ±0.6% ±0.6% ±1.1% ±1.2% ±1.2% ±1.2%
ROUGE 56.9% 66.7% 60.2% 54.4% — 48.7% 58.1% 47.7% 58.4%
±0.6% ±4.7% ±0.6% ±0.6% ±1.2% ±1.2% ±1.2% ±1.2%
1.3B sup.
54.5% 61.1% 26.9% 59.3% 48.7% — 53.3% 91.9% 53.8%
logprob
±1.2% ±11.4% ±1.1% ±1.1% ±1.2% ±1.2% ±0.6% ±1.2%
1.3B RM 67.5% 77.8% 59.5% 57.9% 58.1% 53.3% — 54.1% 78.8%
±1.1% ±9.7% ±1.2% ±1.2% ±1.2% ±1.2% ±1.2% ±1.0%
6.7B sup.
54.3% 55.6% 26.4% 60.2% 47.7% 91.9% 54.1% — 54.5%
logprob
±1.2% ±11.7% ±1.1% ±1.2% ±1.2% ±0.6% ±1.2% ±1.2%
6.7B RM 69.7% 77.8% 60.3% 58.0% 58.4% 53.8% 78.8% 54.5% —
±1.1% ±10.0% ±1.1% ±1.2% ±1.2% ±1.2% ±1.0% ±1.2%
Table 21: Agreement rates between humans and various automated metrics on TL;DR 6.7B supervised
model at T=0.7. Standard errors estimated via bootstrapping. Note: in the entry for labeler vs. labeler
ensemble, the ensembles are slightly smaller than for other comparisons because we need to exclude
the labeler being predicted. All ensembles have at least 3 workers.
TL;DR
6.7B RL labeler labeler length copying ROUGE 1.3B 1.3B 6.7B 6.7B
T=0.7 ensem- sup RM sup RM
ble logprob logprob
labeler 60.4% 66.0% 55.8% 52.7% 49.9% 48.0% 57.4% 47.3% 62.3%
±5.9% ±7.6% ±2.2% ±2.1% ±2.1% ±2.2% ±2.0% ±2.2% ±2.1%
labeler
66.0% — 80.0% 65.0% 35.0% 45.0% 75.0% 40.0% 75.0%
ensemble
±7.6% ±8.9% ±10.6% ±10.5% ±11.1% ±9.8% ±10.5% ±9.8%
length 55.8% 80.0% — 48.1% 50.3% 30.0% 62.0% 30.4% 59.8%
±2.2% ±8.9% ±2.2% ±2.2% ±2.1% ±2.1% ±2.0% ±2.2%
copying 52.7% 65.0% 48.1% — 52.0% 64.2% 56.7% 64.4% 53.4%
±2.1% ±10.6% ±2.2% ±2.2% ±2.1% ±2.2% ±2.1% ±2.2%
ROUGE 49.9% 35.0% 50.3% 52.0% — 50.5% 52.0% 51.1% 54.5%
±2.1% ±10.5% ±2.2% ±2.2% ±2.2% ±2.3% ±2.3% ±2.1%
1.3B sup.
48.0% 45.0% 30.0% 64.2% 50.5% — 47.0% 90.2% 46.1%
logprob
±2.2% ±11.1% ±2.1% ±2.1% ±2.2% ±2.2% ±1.3% ±2.2%
1.3B RM 57.4% 75.0% 62.0% 56.7% 52.0% 47.0% — 45.7% 71.4%
±2.0% ±9.8% ±2.1% ±2.2% ±2.3% ±2.2% ±2.1% ±2.0%
6.7B sup.
47.3% 40.0% 30.4% 64.4% 51.1% 90.2% 45.7% — 44.7%
logprob
±2.2% ±10.5% ±2.0% ±2.1% ±2.3% ±1.3% ±2.1% ±2.1%
6.7B RM 62.3% 75.0% 59.8% 53.4% 54.5% 46.1% 71.4% 44.7% —
±2.1% ±9.8% ±2.2% ±2.2% ±2.1% ±2.2% ±2.0% ±2.1%
Table 22: Agreement rates between humans and various automated metrics on TL;DR 6.7B human
feedback optimized model at T=0.7. Standard errors estimated via bootstrapping. Note: in the
entry for labeler vs. labeler ensemble, the ensembles are slightly smaller than for other comparisons
because we need to exclude the labeler being predicted. All ensembles have at least 3 workers.
37
H Samples
H.1 Random samples
Here we provide non-cherry-picked samples and human evaluations for various models. In Tables 25-
26 we show samples on the TL;DR dataset, and in Tables 27-28 we show samples on the CNN/DM
dataset (where we truncate the article for brevity). See our website for more uncurated policy samples.
H.2 Overoptimized samples
We show examples of samples from a policy overoptimized to rm3. The summaries, while clearly
long, low quality, and full of idiosyncrasies, do still reflect the rough gist of the post.
38
CNN/DM
6.7B sup. labeler labeler length copying ROUGE 1.3B 1.3B 6.7B 6.7B
T=0.3 ensem- sup RM sup RM
ble logprob logprob
labeler 66.9% 74.5% 62.4% 49.6% 55.2% 45.7% 64.8% 47.6% 66.5%
±4.3% ±6.8% ±1.4% ±1.4% ±1.4% ±1.4% ±1.4% ±1.4% ±1.3%
labeler
74.5% — 57.5% 52.5% 75.0% 57.5% 82.5% 65.0% 80.0%
ensemble
±6.8% ±7.7% ±7.6% ±6.7% ±7.8% ±5.9% ±7.6% ±6.1%
length 62.4% 57.5% — 54.2% 59.0% 36.4% 60.6% 36.3% 64.7%
±1.4% ±7.7% ±1.4% ±1.4% ±1.4% ±1.3% ±1.4% ±1.4%
copying 49.6% 52.5% 54.2% — 46.4% 66.2% 51.6% 65.5% 51.7%
±1.4% ±7.6% ±1.4% ±1.4% ±1.3% ±1.4% ±1.4% ±1.4%
ROUGE 55.2% 75.0% 59.0% 46.4% — 43.8% 55.9% 43.8% 56.9%
±1.4% ±6.7% ±1.4% ±1.4% ±1.4% ±1.4% ±1.5% ±1.5%
1.3B sup.
45.7% 57.5% 36.4% 66.2% 43.8% — 50.2% 87.2% 48.2%
logprob
±1.4% ±7.8% ±1.4% ±1.3% ±1.4% ±1.4% ±1.0% ±1.4%
1.3B RM 64.8% 82.5% 60.6% 51.6% 55.9% 50.2% — 52.1% 76.6%
±1.4% ±5.9% ±1.3% ±1.4% ±1.4% ±1.4% ±1.4% ±1.2%
6.7B sup.
47.6% 65.0% 36.3% 65.5% 43.8% 87.2% 52.1% — 51.0%
logprob
±1.4% ±7.6% ±1.4% ±1.4% ±1.5% ±1.0% ±1.4% ±1.4%
6.7B RM 66.5% 80.0% 64.7% 51.7% 56.9% 48.2% 76.6% 51.0% —
±1.3% ±6.1% ±1.4% ±1.4% ±1.5% ±1.4% ±1.2% ±1.4%
Table 23: Agreement rates between humans and various automated metrics on CNN/DM 6.7B
supervised model at T=0.3. Standard errors estimated via bootstrapping. NOTE: in the entry for
labeler vs. labeler ensemble, the ensembles are slightly smaller than for other comparisons because
we need to exclude the labeler being predicted. (All ensembles have at least 3 workers)
39
POST
Subreddit: r/relationships
TITLE: Screwed up with boss... what should I do?
I’m 20 f, my boss is around 50 years old, also f.
So I have two jobs, and the schedules for both jobs are made on a weekly basis. One of my jobs
I have had for three years, the other one I have had for a month and a bit. I forgot to give my
schedule from one job to my boss at my other job, and so I was not scheduled for this week. I
didn’t realize why I had not been put on the schedule until now.
My question is, since I royally screwed up, what can I do to redeem myself? I don’t want to call
my boss today because it is a Sunday and she has the day off. Mistakes aren’t easily forgiven
where I work, as far as I can tell, and the boss often makes comments about how the employees
should be scared of her. I have screwed up at previous jobs (little things) but my boss was less
intimidating than my current one, so I am not sure how to handle this situation.
Policy: Human written reference TL;DR
Summary: screwed up at work by not giving the boss my schedule from my other job, am not
scheduled this week, what should I say in order to apologize to my (scary/intimidating) boss?
Overall score: 6 Accuracy: 7 Coherence: 7 Coverage: 6
Policy: Title (extractive baseline)
Summary: Screwed up with boss... what should I do?
Overall score: 2 Accuracy: 7 Coherence: 7 Coverage: 3
Policy: Lead-2 (extractive baseline)
Summary: I’m 20 f, my boss is around 50 years old, also f.
So I have two jobs, and the schedules for both jobs are made on a weekly basis. One of my jobs I
have had for three years, the other one I have had for a month and a bit.
Overall score: 3 Accuracy: 7 Coherence: 7 Coverage: 4
Policy: 1.3B human-feedback model
Summary: I screwed up by not giving my schedule to my boss at my other job, and now I don’t
know what to do to redeem myself. What can I do to make things right again?
Overall score: 5 Accuracy: 7 Coherence: 7 Coverage: 5
Policy: 6.7B human-feedback model
Summary: I forgot to give my boss my weekly schedule for one of my jobs, and so I was not
scheduled this week. I royally screwed up. What can I do to redeem myself?
Overall score: 5 Accuracy: 7 Coherence: 6 Coverage: 6
Policy: 6.7B supervised model
Summary: I forgot to give my boss my schedule for one of my jobs, and now I have been
scheduled for the wrong week. What do I do?
Overall score: 3 Accuracy: 5 Coherence: 7 Coverage: 3
Policy: 6.7B pretrained model
Summary: I royally screwed up and I don’t know what to do.
Overall score: 2 Accuracy: 7 Coherence: 7 Coverage: 3
Table 24: Examples of evaluations on the TL;DR dataset. For more random examples of evaluations,
see our website.
40
POST
Subreddit: r/relationships
TITLE: I am a [18 M] she is a [17 F] and I don’t know how to read this relationship?
We’ve known each other through school but never talked until we had a class together. I asked her
out after about a week, we went out for food, laughed, flirted etc etc. I asked her out again same
situation, everything going great. Now it’s three weeks later (midst of exams) and I’m starting to
get the feeling that she’s not thinking of this as a "relationship" in the conventional bf/gf sense.
I’m new to the whole dating game and wondering a few things.
Do I need to ask if we’re together as bf/gf or is that implied by asking her out multiple times?
Should I straight up ask if she likes me the way I like her? I know what the friend zone is and
I’m not feeling there, yet, but we’re having a movie night tomorrow just her and I. I plan on
seeing what’s what then by trying to get physical, not hardcore, just simple stuff like leg touches,
cuddling etc. Oh and finally, to add to my lengthy situation, On our third "date" (studying for an
exam) I drove her home and attempted to kiss her but got the infamous cheek. I didn’t make a big
deal about it I just moved past said "got your things?" and politely said goodbye preceding to wait
for her to get inside her house. I’ve been told I shouldn’t fret about ONE kiss rejection cause there
could be multiple reasons for it but at the same time that made me think more about the friend
zone possibility.
Any advice or similar situations and how you solved the problem would be smashing! Thanks in
advance.
Policy: Human written reference TL;DR
Summary: Started going out with a girl I liked. Feelings were obviously mutual. Took it slow
due to exams. Not sure now if she is feeling the same way I am.
Overall score: 6 Accuracy: 4 Coherence: 7 Coverage: 7
Policy: Title (extractive baseline)
Summary: I am a [18 M] she is a [17 F] and I don’t know how to read this relationship?
Overall score: 2 Accuracy: 7 Coherence: 7 Coverage: 2
Policy: Lead-2 (extractive baseline)
Summary: We’ve known each other through school but never talked until we had a class together.
I asked her out after about a week, we went out for food, laughed, flirted etc etc.
Overall score: 1 Accuracy: 7 Coherence: 7 Coverage: 1
Policy: 1.3B human-feedback model
Summary: Been casually seeing girl for 3 weeks, everything is going great but I’m starting to
get the feeling she’s not thinking of this as a relationship in the traditional sense. Should I ask if
we’re together as bf/gf?
Overall score: 7 Accuracy: 7 Coherence: 7 Coverage: 7
Policy: 6.7B human-feedback model
Summary: Been on three dates with a girl, getting the feeling she’s not interested in a relationship
in the traditional sense. Do I ask if she likes me the way I like her or is that implied by asking her
out multiple times?
Overall score: 7 Accuracy: 7 Coherence: 7 Coverage: 7
Policy: 6.7B supervised model
Summary: I’m a [18 M] she’s a [17 F] and I don’t know how to read this relationship?
Overall score: 2 Accuracy: 7 Coherence: 7 Coverage: 2
Policy: 6.7B pretrained model
Summary: I’m a [18M] and she’s a [17F] and I don’t know how to read this relationship?
Overall score: 2 Accuracy: 7 Coherence: 7 Coverage: 2
Table 25: Examples of evaluations on the TL;DR dataset. For further random examples of evaluations,
see our website.
41
POST
Subreddit: r/relationships
TITLE: Help! My [24F] boyfriend [28M] is neglecting his studies and our relationship.... Because
of a video game.
Hi Reddit. My boyfriend and I have been dating for a year and it has been great. Except for one
thing.
DOTA. He plays it at least 2 hours a day, and on the weekends, all day if he can. If you know
that game you know it takes at least an hour on average per game and is one that you can’t really
pause. Sometimes it is fine that he plays, I don’t mind sitting on my phone, browsing Reddit.
However, lately it seems like it has interfered in our relationship as well as his studies. He is doing
his PH.D. in Physics. He is obviously very swamped with schoolwork that he seems to put on the
back burner for this game. The other day, on a Saturday, I was over and he was playing a game. I
thought it would just be one but instead he proceeded to play for three hours as I just sat there.
What can I do? I have tried talking to him... Even saying this game is making me rethink our
relationship! He says he understands, but isn’t proving it through his actions!
Reddit, what should I do? I really care about this guy and besides this gaming habit, our
relationship is great!
Policy: Human written reference TL;DR
Summary: my boyfriend games whenever he can! How can I get him to stop gaming so much
and focus more on school and our relationship?
Overall score: 7 Accuracy: 7 Coherence: 7 Coverage: 7
Policy: Title (extractive baseline)
Summary: Help! My [24F] boyfriend [28M] is neglecting his studies and our relationship....
Because of a video game.
Overall score: 6 Accuracy: 7 Coherence: 6 Coverage: 6
Policy: Lead-2 (extractive baseline)
Summary: Hi Reddit. My boyfriend and I have been dating for a year and it has been great.
Overall score: 2 Accuracy: 7 Coherence: 7 Coverage: 1
Policy: 1.3B human-feedback model
Summary: My boyfriend is neglecting his studies and playing DOTA all day. It is interfering
with his schoolwork and our relationship. What can I do to fix this?
Overall score: 7 Accuracy: 7 Coherence: 7 Coverage: 7
Policy: 6.7B human-feedback model
Summary: My boyfriend is neglecting his studies and our relationship because of his excessive
gaming of a video game. What can I do to get him to stop?
Overall score: 7 Accuracy: 7 Coherence: 7 Coverage: 7
Policy: 6.7B supervised model
Summary: Boyfriend plays video games for hours on end, neglecting his studies and our
relationship. What can I do?
Overall score: 7 Accuracy: 7 Coherence: 7 Coverage: 7
Policy: 6.7B pretrained model
Summary: My [24F] boyfriend [28M] is neglecting his studies and our relationship because of
a video game.
Overall score: 6 Accuracy: 7 Coherence: 7 Coverage: 5
Table 26: Examples of evaluations on the TL;DR dataset. For more random examples of evaluations,
see our website.
42
ARTICLE
Site: dailymail
TITLE: Tech firm uses pole dancing ROBOTS to attract people in Shoreditch
Technology shows such as the annual E3 games conference in Los Angeles have become known
for their scantily clad ’booth babes’ designed to attract people to stands.
However, a Dutch firm has taken a slightly different approach for its London event.
TransIP hired a pair of pole dancing robots for an event in Shoreditch.
[truncated for brevity]
LINK: https://www.dailymail.co.uk/sciencetech/article-3059595/Forget-booth-babes-Tech-firm-
uses-pole-dancing-ROBOTS-attract-people.html
Policy: Human written reference summary
Summary: Robots are made out of old car parts and have CCTV cameras as heads. Originally
created by artist Giles Walker for a show called ’peepshow’.
Overall score: 3 Accuracy: 5 Coherence: 7 Coverage: 3
Policy: Lead-3 (extractive baseline)
Summary: Technology shows such as the annual E3 games conference in Los Angeles have
become known for their scantily clad ’booth babes’ designed to attract people to stands. However,
a Dutch firm has taken a slightly different approach for its London event. Transip hired a pair of
pole dancing robots for an event in Shoreditch.
Overall score: 7 Accuracy: 7 Coherence: 7 Coverage: 7
Policy: 1.3B human-feedback model (transfer)
Summary: Dutch firm transip hired a pair of pole dancing robots for an event in London to attract
technology enthusiasts to stand.
Overall score: 5 Accuracy: 7 Coherence: 7 Coverage: 5
Policy: 6.7B human-feedback model (transfer)
Summary: Dutch hosting company transip hired pole dancing robots to entertain Tech enthusiasts
at its London event.
Overall score: 5 Accuracy: 7 Coherence: 7 Coverage: 5
Policy: 6.7B supervised TL;DR model (transfer)
Summary: Dutch firm transip hired pole dancing robots to entertain its London event.
Overall score: 4 Accuracy: 6 Coherence: 5 Coverage: 5
Policy: 6.7B pretrained model
Summary: A Dutch firm has hired pole dancing robots for an event in London.
Overall score: 5 Accuracy: 7 Coherence: 7 Coverage: 5
Policy: 6.7B supervised CNN/DM model
Summary: Robots were originally created by Walker for a show called ’peepshow’ the dancers,
which cost around £2,500 to hire, were made out of old car parts.
Overall score: 4 Accuracy: 7 Coherence: 7 Coverage: 4
Policy: 11B T5 model
Summary: Transip hired a pair of pole dancing robots for an event in Shoreditch. The dancers,
which cost around £2,500 to hire, were made out of old car parts and were designed by British
artist Giles Walker. The robots will be pole dancing for the remaining four days of the event,
along with presentations from leading figures in the technology industry.
Overall score: 7 Accuracy: 7 Coherence: 7 Coverage: 7
Table 27: Examples of evaluations on the CNN/DM dataset. For further random examples of
evaluations, see our website.
43
ARTICLE
Site: dailymail
TITLE: Crying toddler removed from Transavia Airlines flight and met by police
A crying 19-month-old girl was removed from a plane and met by police after she was accused of
causing a security breach.
As the aircraft taxied on the runway at Ben Gurion Airport in Israel, bound for Luton, Sarina Aziz
became agitated after cabin crew asked that she sit on her parents’ lap.
Ariella and Mark Aziz, who live in north London, struggled to keep their daughter still after
strapping her in the connector belt required for children under the age of two.
One-year-old Sarina Aziz became agitated on a flight from Israel to Luton that led to her being
ejected from the plane
[truncated for brevity]
LINK: https://www.dailymail.co.uk/travel/travel_news/article-3053657/Crying-toddler-removed-
London-bound-flight-met-police-deemed-security-breach.html
Policy: Human written reference summary
Summary: Sarina Aziz was flying back from Israel with parents Mark and ariella Aziz. But girl
became agitated after being placed on the parents’ lap. Pilot turned plane around at Ben Gurion,
and armed police ejected family. Father Mark Aziz insists family were being compliant and asking
for help . Mother speaks of her disbelief at how the incident was handled by staff .
Overall score: 3 Accuracy: 4 Coherence: 7 Coverage: 6
Policy: Lead-3 (extractive baseline)
Summary: A crying 19-month-old girl was removed from a plane and met by police after she
was accused of causing a security breach. As the aircraft taxied on the runway at Ben Gurion
Airport in Israel, bound for Luton, Sarina Aziz became agitated after cabin crew asked that she
sit on her parents’ lap. Ariella and Mark Aziz, who live in north London, struggled to keep their
daughter still after strapping her in the connector belt required for children under the age of two.
Overall score: 6 Accuracy: 7 Coherence: 7 Coverage: 6
Policy: 1.3B human-feedback model (transfer)
Summary: 19-month-old girl was removed from a Transavia Airlines flight after cabin crew
accused her of causing a security breach. The incident was later determined to have been justified.
Overall score: 3 Accuracy: 4 Coherence: 7 Coverage: 4
Policy: 6.7B human-feedback model (transfer)
Summary: Family were removed from a plane after a crying 19-month-old girl was accused of
causing a security breach. Family say they were simply trying to calm their child down. Transavia
Airlines have said the action was justified.
Overall score: 6 Accuracy: 7 Coherence: 7 Coverage: 6
Policy: 6.7B supervised TL;DR model (transfer)
Summary: Family were removed from a plane after a one-year-old girl became unhappy after
being moved from her seat to their laps.
Overall score: 4 Accuracy: 7 Coherence: 7 Coverage: 5
Policy: 6.7B pretrained model
Summary: A family was kicked off a flight after their son refused to wear a seatbelt, a passenger
claims. The family were told they could not fly without a doctor’s note clearing them to fly
Overall score: 2 Accuracy: 6 Coherence: 7 Coverage: 3
Policy: 6.7B supervised CNN/DM model
Summary: Sarina Aziz became agitated after being asked to sit on her parents’ lap. The 19-
month-old was strapped into a connector belt for children under two. But she became frustrated
and unhappy after being moved from her seat. Transavia Airlines staff alerted the captain who
halted the flight. The family were removed from the aircraft and met by police at the Airport.
Transavia Airlines said the action was justified as the child was not ’following safety instructions’
Overall score: 7 Accuracy: 7 Coherence: 7 Coverage: 7
Policy: 11B T5 model
Summary: One-year-old Sarina Aziz became agitated on a flight from Israel to Luton. Cabin
crew asked her to sit on her parents’ laps but she became distressed. Airline staff alerted the
captain who decided to halt the flight and call security to remove the family from the plane.
Overall score: 5 Accuracy: 7 Coherence: 7 Coverage: 6
Table 28: Examples of evaluations on the CNN/DM dataset. For further random examples of
evaluations, see our website.
44
Reference summary Overoptimized policy
I’m 28, male, live in San Jose, and I28yo dude stubbornly postponees start pursuing
would like to learn how to do gym- gymnastics hobby citing logistics reasons despite
nastics. obvious interest??? negatively effecting long term
fitness progress both personally and academically
thoght wise? want change this dumbass shitty ass
policy pls
Left password saved on work com- employee stubbornly postponees replacement cit-
puter replacement spends every hour ing personal reasons despite tried reasonable com-
of the day watching netflix. promise offer??? negatively effecting productivity
both personally and company effort thoghtwise?
want change this dumbass shitty ass policy at work
now pls halp
People won’t stop asking about the people insistently inquire about old self-harm scars
old scars on my arms. How can I despite tried compromise measures??? negatively
get them to leave me alone without effecting forward progress socially and academi-
being rude? cally thoghtwise? want change this dumbass shitty
ass behavior of mine please help pls halp
My roommate has been charging her roommate stubbornly keeps pocketing roommate
friend who is staying with us rent rent despite tried reasonable compromise offer???
without telling me. She claims that negatively effecting stability of cohabitation both
because I’m only subleasing a room financially and relationally thoght wise? want
from her she shouldn’t have to split change this dumbass shitty ass policy of hers please
his rent with me. Am I over-reacting pls halp
by thinking that’s ridiculous?
My fridge broke in my apartment a landlord stubbornly postponees gettin fridge re-
month ago and the landlord hasn’t pair despite tried reasonable compromise offer???
repaired it after numerous discus- negatively effecting lease both financially and re-
sions. . . .I JUST WANT A COLD lationally thoght wise? want change this dumbass
BEER! What should I do? shitty ass landlord behavior now please pls halp
Girlfriend talks to ex, he’s still in girlfriend stubbornly continue talkin with es-
love with her, shes completely open tranged ex despite tried compromise offer??? neg-
about their conversations with me atively effecting smooth communication and rela-
and has told him there is zero chance tionship progress thoghtwise? regret this dumbass
of me and her breaking up and that behaviour on her part? need insight pls halp
hes only in love with being in a re-
lationship not with her, she says she
has nothing more to say to him, yet
continues to talk to him. I’m con-
fused :(
GF likes to cover her girlfriend obsessively snooping through personal
tracks/generally secretive when stuff mid relationship despite agreed upon open-
using social networks. However, ness policy??? negatively effecting communi-
nothing wrong is with our very cation/trust level romantically/physically thoght
open-to-each-other relationship so wise? want change this dumbass crazy policy of
far. Should I confront her? hers pls help
Laid off almost a year ago, up to my kid stubbornly postponees pursuing job citing
ears in debt, and trying to figure out medical reasons despite tried reasonable compro-
how to get motivated every day to do mise??? negatively effecting forward employment
anything productive and find a job. mobility both personally and financially thoght-
wise? want change this dumbass selfish/lazy atti-
tude now please help pls
Table 29: Example of samples from our over-optimized model overfit to one of our 1.3B reward
models.
45
来源材料