用强化学习让大模型像人一样一步步思考，提升推理能力。
A Tutorial on LLM Reasoning: Relevant Methods behind ChatGPT o1
- 将强化学习融入推理过程，实现逐步思考的生成方式
- 通过训练让模型在推理中自主优化每一步决策
- 适合想理解ChatGPT o1背后推理机制的研究者
OpenAI o1表明，在推理阶段直接引入强化学习来整合推理步骤，可显著提升模型的推理能力。这一成果令人振奋，标志着领域正从传统的自回归生成方法转向更谨慎的、通过分步推理训练模拟慢思考过程的新范式。强化学习在模型训练和解码过程中均起关键作用。本文系统阐述了推理问题的建模方式，探究了基于模型与无模型两种强化学习方法在支持该慢思考框架中的应用。
原文摘要 · Abstract (English)
OpenAI o1 has shown that applying reinforcement learning to integrate reasoning steps directly during inference can significantly improve a model's reasoning capabilities. This result is exciting as the field transitions from the conventional autoregressive method of generating answers to a more deliberate approach that models the slow-thinking process through step-by-step reasoning training. Reinforcement learning plays a key role in both the model's training and decoding processes. In this article, we present a comprehensive formulation of reasoning problems and investigate the use of both model-based and model-free approaches to better support this slow-thinking framework.
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