Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought (2501.04682v1)

Abstract: 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.

• The paper presents Meta-CoT, a framework that enhances LLM reasoning by embedding systematic search techniques into the process.
• It utilizes process supervision, synthetic data generation, and reinforcement learning to train models for non-linear, complex problem solving.
• The framework paves the way for novel reasoning algorithms that improve AI's capacity to handle intricate tasks with human-like, deliberate thought.

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

This paper explores the development of a framework, Meta Chain-of-Thought (Meta-CoT), designed to enhance the reasoning capabilities of LLMs beyond the traditional Chain-of-Thought (CoT) paradigm. CoT has shown some effectiveness in handling relatively simple reasoning tasks by encouraging models to "think" step-by-step, thereby generating intermediate reasoning steps that help in formulating the final answer. However, as this paper argues, CoT methods typically fail to fully capture the complex, non-linear, and often iterative nature of reasoning required for solving more intricate problems.

Introduction: Limitations of Current Methods

The authors begin by highlighting the limitations of state-of-the-art LLMs in handling complex reasoning tasks, particularly those that require more than linear reasoning. They suggest that traditional CoT models do not accurately represent the latent, explorative, and verification-intensive reasoning processes which professionals engage in when solving complex problems. The need to accurately model and train LLMs to emulate such a process is emphasized.

Meta-CoT Framework

To address these challenges, the paper proposes the Meta-CoT framework which explicitly models the reasoning process required to generate high-complexity outcomes. The authors describe Meta-CoT as a method that integrates systematic search processes, internalizing them within an auto-regressive model to simulate human-like reasoning. Meta-CoT is positioned as a step towards System 2 reasoning in cognitive science terms, a type of deliberate, logic-based reasoning characterized by its computational depth and non-linear form.

Methodological Contributions

The authors of this paper present several methodological contributions:

• Meta-CoT Generation: They propose techniques for generating Meta-CoT using process supervision, synthetic data generation, and search algorithms such as Monte Carlo Tree Search (MCTS) and A*.
• Training Pipeline: A concrete pipeline is outlined for training LLMs to produce Meta-CoTs, using instruction tuning and reinforcement learning.
• Empirical Evaluation: The paper substantiates its claims with empirical results showcasing state-of-the-art models performing tasks with internalized in-context search processes.

Implications and Open Questions

This work offers practical and theoretical implications for future AI development:

• Algorithmic Insight: The integration of search processes within LLM training points toward potential breakthroughs in enhancing model reasoning capabilities.
• Scaling Laws and Efficiency: Addressing open research questions about the scaling laws of reasoning and the roles of verification and search within models could significantly impact AI's ability to perform complex reasoning tasks more efficiently.
• Discovery of Novel Reasoning Algorithms: Through the framework, there is potential for uncovering new reasoning methodologies, especially when combined with neural networks and extensive computational resources.

Speculation on Future Developments

The paper speculates on future advancements where the Meta-CoT framework could learn and apply entirely new logical reasoning processes, thus outperforming traditional reasoning models that are bounded by search complexity. The exploration of backtracking and recursive introspection in model generation is highlighted as a frontier for further research.

Conclusion

By providing a roadmap for developing more sophisticated reasoning frameworks within LLMs, this paper contributes significantly to the discourse on artificial intelligence's capability to emulate and, perhaps, exceed human reasoning processes. The Meta-CoT framework stands out as an ambitious yet promising path toward achieving higher-order cognitive functions in AI systems, potentially leading to advances in problem-solving tasks across scientific, mathematical, and complex analytical domains.