Planning with MCTS: Enhancing Problem-Solving in Large Language Models
Keywords: Large Language Models, Monte Carlo Tree Search, Planning, Chain-of-Thought
TL;DR: This paper proposes a novel framework using Monte Carlo Tree Search to generate better plans, significantly improving Large Language Models' problem-solving accuracy on complex reasoning tasks.
Abstract: Despite recent advances in Large Language Models (LLMs), their ability to solve complex reasoning problems remains limited by inconsistent planning and logical flaws. We present a novel framework that significantly enhances LLMs' problem-solving capabilities by leveraging Monte Carlo Tree Search (MCTS) for plan generation. Unlike previous approaches that apply MCTS to solution search, our method uniquely integrates MCTS into the planning phase, guided by specialized LLM-powered agents that evaluate plan quality. Experiments across diverse benchmark datasets demonstrate that our approach improves problem-solving accuracy by an average of 40.59\% compared to zero-shot Chain-of-Thought prompting. Furthermore, we show that using smaller models for MCTS planning and larger models for execution can maintain high performance while reducing computational costs. This work opens new avenues for developing more robust and efficient AI systems capable of tackling complex real-world problems, with potential applications in fields requiring advanced logical reasoning and long-term planning. Our code examples are publicly available at the Anonymous Github Repository.
Primary Area: causal reasoning
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Submission Number: 6505
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