Scaling laws

英文原文文件：scaling_laws.md

计算解释

指导密集训练预算在计算量、数据量与模型规模之间如何分配的经验规则。

支撑阅读卡

• Language Models are Unsupervised Multitask Learners (2019, hyperscale_dense_llm_training)
• Language Models are Few-Shot Learners (2020, hyperscale_dense_llm_training)
• Scaling Laws for Neural Language Models (2020, hyperscale_dense_llm_training)
• GLaM: Efficient Scaling of Language Models with Mixture-of-Experts (2021, sparse_memory_efficient_scaling)
• Training Compute-Optimal Large Language Models (2022, hyperscale_dense_llm_training)
• PaLM: Scaling Language Modeling with Pathways (2022, hyperscale_dense_llm_training)
• Gemini: A Family of Highly Capable Multimodal Models (2023, hyperscale_dense_llm_training)
• Textbooks Are All You Need (2023, hyperscale_dense_llm_training)
• A Survey of Large Language Models (2023, hyperscale_dense_llm_training)
• The Llama 3 Herd of Models (2024, hyperscale_dense_llm_training)
• Qwen3 Technical Report (2025, hyperscale_dense_llm_training)

后续计算范式下过时或退居次要的内容

仅通过已链接的阅读卡追踪，不将本方法页视为独立证据来源。