Search and simulation

英文原文文件：search.md

计算解释

在推理或训练过程中，基于已学习的策略、价值函数、模型或科学评分组件所进行的结构化探索。

支撑阅读卡

• Human-level control through deep reinforcement learning (2015, search_simulation_science_compute)
• Mastering the game of Go with deep neural networks and tree search (2016, search_simulation_science_compute)
• Mastering the game of Go without human knowledge (2017, search_simulation_science_compute)
• A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play (2018, search_simulation_science_compute)
• Grandmaster level in StarCraft II using multi-agent reinforcement learning (2019, search_simulation_science_compute)
• Dota 2 with Large Scale Deep Reinforcement Learning (2019, search_simulation_science_compute)
• Mastering Atari, Go, chess and shogi by planning with a learned model (2019, search_simulation_science_compute)
• Improved protein structure prediction using potentials from deep learning (2020, search_simulation_science_compute)
• Highly accurate protein structure prediction with AlphaFold (2021, search_simulation_science_compute)
• Tree of Thoughts: Deliberate Problem Solving with Large Language Models (2023, inference_time_compute_post_training)
• Accurate structure prediction of biomolecular interactions with AlphaFold 3 (2024, search_simulation_science_compute)
• Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2 (2025, search_simulation_science_compute)
• AlphaEvolve: A coding agent for scientific and algorithmic discovery (2025, search_simulation_science_compute)

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。