具有强化学习的奥林匹克级正式数学推理
Thomas Hubert1, Rishi Mehta2, Laurent Sartran2
1Google DeepMind, London, UK. tkhubert@google.com.
Nature
|November 12, 2025
概括
一个人工智能代理AlphaProof通过强化学习 (RL) 和正式证明来学习复杂的数学推理. 这一系统在国际海事组织的比赛中获得了奖牌级别的表现,
科学领域:
- 人工智能
- 正式的方法
- 强化学习
背景情况:
- 目前的人工智能系统通常缺乏数学推理的正式验证.
- 像Lean这样的正式语言提供了有基础的推理环境.
- 强化学习 (RL) 是一种在互动环境中学习的机制.
研究的目的:
- 开发一个能够进行复杂数学推理和正式证明的AI系统.
- 在正式的数学领域利用RL学习证明策略.
- 提高人工智能在挑战性数学问题上的表现.
主要方法:
- 开发了AlphaProof,一个以AlphaZero为灵感的代理,利用RL进行正式的证据发现.
- 在数以百万计的自动化数学问题上训练AlphaProof.
- 使用测试时间 RL 针对难题进行特定的调整.
主要成果:
- 在历史数学竞赛问题上取得了显著的先进成果.
- 在2024年国际海事组织比赛中,人工智能系统解决了五个非几何问题中的三个,包括最困难的问题.
- 结合AlphaGeometry 2,人工智能获得了相当于银牌的分数,这是人工智能首次获得奖牌级别的成绩.
结论:
- 从有基础的经验中进行大规模的学习使得人工智能代理具有复杂的数学推理.
- 对于复杂的数学问题解决,AlphaProof展示了可靠的人工智能工具的潜力.
- 这项工作为人工智能系统解决复杂的数学挑战铺平了道路.
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