Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though

Violet Xiang, Charlie Snell, Kanishk Gandhi, Alon Albalak, Anikait Singh, Chase Blagden, Duy Phung, Rafael Rafailov, nathan lile, SynthLabs.ai Research Team, Louis Castricato, Jan-Philipp Franken, Nick Haber, Chelsea Finn

Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though: 99 upvotes on Hugging Face Daily Papers, #2 of 13 papers on 2025-01-09. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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