We propose a novel framework, Meta Chain-of-Thought (Meta-CoT), which extends traditional Chain-of-Thought (CoT) by explicitly modeling the underlying reasoning required to arrive at a particular CoT. We present empirical evidence from state-of-the-art models exhibiting behaviors consistent with in-context search, and explore methods for producing Meta-CoT via process supervision, synthetic data generation, and search algorithms. Finally, we outline a concrete pipeline for training a model to produce Meta-CoTs, incorporating instruction tuning with linearized search traces and reinforcement learning post-training. Finally, we discuss open research questions, including scaling laws, verifier roles, and the potential for discovering novel reasoning algorithms. This work provides a theoretical and practical roadmap to enable Meta-CoT in LLMs, paving the way for more powerful and human-like reasoning in artificial intelligence.

该研究解决了现有链式思维模型在推理过程中的局限性，提出了一种新的框架——元链式思维（Meta-CoT），通过明确建模推理过程来增强传统链式思维。研究结果表明，Meta-CoT可以通过过程监督、合成数据生成和搜索算法有效生成，有助于在大型语言模型中实现更强大的人类推理能力。

朝着系统2推理的LLMs：学会使用元连锁思维