Towards Self-Improvement of LLMs via MCTS: Leveraging Stepwise Knowledge
  with Curriculum Preference Learning
Authors: name: Xiyao Wang, name: Linfeng Song, name: Ye Tian, name: Dian Yu, name: Baolin Peng, name: Haitao Mi, name: Furong Huang, name: Dong Yu

Monte Carlo Tree Search (MCTS) has recently emerged as a powerful technique
for enhancing the reasoning capabilities of LLMs. Techniques such as SFT or DPO
have enabled LLMs to distill high-quality behaviors from MCTS, improving their
reasoning performance. However, existing distillation methods underutilize the
rich trajectory information generated by MCTS, limiting the potential for
improvements in LLM reasoning. In this paper, we propose AlphaLLM-CPL, a novel
pairwise training framework that enables LLMs to self-improve through MCTS
behavior distillation. AlphaLLM-CPL efficiently leverages MCTS trajectories via
two key innovations: (1) AlphaLLM-CPL constructs stepwise trajectory pairs from
child nodes sharing the same parent in the search tree, providing step-level
information for more effective MCTS behavior distillation. (2) AlphaLLM-CPL
introduces curriculum preference learning, dynamically adjusting the training
sequence of trajectory pairs in each offline training epoch to prioritize
critical learning steps and mitigate overfitting. Experimental results on
mathematical reasoning tasks demonstrate that AlphaLLM-CPL significantly
outperforms previous MCTS behavior distillation methods, substantially boosting
the reasoning capabilities of LLMs.