Interpretable Contrastive Monte Carlo Tree Search Reasoning

Zitian Gao, Boye Niu, Xuzheng He, Haotian Xu, Hongzhang Liu, Aiwei Liu, Xuming Hu, Lijie Wen

Keywords: Monte Carlo Tree Search, Large Language Models, Multi-Step Reasoning

TL;DR: We propose a novel MCTS reasoning algorithm for LLMs, significantly improves both reasoning accuracy and speed.

Abstract: We propose $\textbf{(S)}peculative \textbf{(C)}ontrastive$ $\textbf{MCTS}^\mathbf{*}$: a novel Monte Carlo Tree Search (MCTS) reasoning algorithm for Large Language Models (LLMs) which significantly improves both reasoning accuracy and speed. Our motivation comes from: 1. Previous MCTS LLM reasoning works often overlooked its biggest drawback—slower speed compared to CoT; 2. Previous research mainly used MCTS as a tool for LLM reasoning on various tasks with limited quantitative analysis or ablation studies of its components from reasoning interpretability perspective. 3. The reward model is the most crucial component in MCTS, however previous work has rarely conducted in-depth study or improvement of MCTS's reward models. Thus, we conducted extensive ablation studies and quantitative analysis on components of MCTS, revealing the impact of each component on the MCTS reasoning performance of LLMs. Building on this, (i) we designed a highly interpretable reward model based on the principle of contrastive decoding and (ii) achieved an average speed improvement of 51.9\% per node using speculative decoding. Additionally, (iii) we improved UCT node selection strategy and backpropagation used in previous works, resulting in significant performance improvement. We outperformed o1-mini by an average of 17.4\% on the Blocksworld multi-step reasoning dataset using Llama-3.1-70B with SC-MCTS\*.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 9705