Boosting Multimodal Reasoning with MCTS-Automated Structured Thinking
Authors: name: Jinyang Wu, name: Mingkuan Feng, name: Shuai Zhang, name: Ruihan Jin, name: Feihu Che, name: Zengqi Wen, name: Jianhua Tao

Multimodal large language models (MLLMs) exhibit impressive capabilities but
still face challenges in complex visual reasoning. While recent efforts attempt
to enhance MLLMs' reasoning by incorporating OpenAI o1-like structured thinking
through explicit search structures or teacher-guided distillation, they often
struggle to balance performance and efficiency. A critical limitation is their
heavy reliance on extensive data and search spaces, resulting in low-efficiency
implicit insight extraction and data utilization. To address this, we propose
AStar, an Automated Structured thinking paradigm for multimodal reasoning via
Monte Carlo Tree Search (MCTS). AStar automatically derives high-level
cognitive reasoning patterns from limited data using MCTS-powered hierarchical
structures. Building on these explicit patterns, we design a unified reasoning
framework that seamlessly integrates models' internal reasoning capabilities
and external reasoning guidelines, enabling efficient inference with minimal
tree iterations. This novel paradigm strikes a compelling balance between
performance and efficiency. Extensive experiments demonstrate AStar's
effectiveness, achieving superior accuracy (54.0%) on the MathVerse
benchmark with a 7B backbone, surpassing GPT-4o (50.2%) while maintaining
substantial data and computational efficiency.