Towards System 2 Reasoning in LLMs: Learning How to Think With Meta
  Chain-of-Though
Authors: name: Violet Xiang, name: Charlie Snell, name: Kanishk Gandhi, name: Alon Albalak, name: Anikait Singh, name: Chase Blagden, name: Duy Phung, name: Rafael Rafailov, name: Nathan Lile, name: Dakota Mahan, name: Louis Castricato, name: Jan-Philipp Franken, name: Nick Haber, name: Chelsea Finn

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.