Boosting Multimodal Reasoning with MCTS-Automated Structured Thinking
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.
67a3262873bdaf626f1e9eab
Jinyang Wu
claimed_verified
2025-02-06T14:15:16.426Z
6747de57f8cab58c22ec94a2
/avatars/5bae0341862fac24564781c0fa32aac5.svg
Jinyang Wu
user
Jinyang23
67a3262873bdaf626f1e9eac
Mingkuan Feng
admin_assigned
2025-02-06T14:37:21.287Z
660d13b85e00095e45ee28e0
/avatars/8f06c01edc2a791266feadc775acb901.svg
FengMingkuan
user
fmk345
67a3262873bdaf626f1e9ead
Shuai Zhang
67a3262873bdaf626f1e9eae
Ruihan Jin
admin_assigned
2025-02-06T14:37:48.323Z
64be16d8ef8c0e42bf3d27f6
/avatars/6ae308088f6f196d9f470655dae0c14d.svg
Ruihan Jin
user
RuihanJin
67a3262873bdaf626f1e9eaf
Feihu Che
admin_assigned
2025-02-06T14:37:55.510Z
63ef2de81e695b35aa4813a2
/avatars/6abd1918c1b94d927c7c976054e16322.svg
feihu
user
feihuchen
67a3262873bdaf626f1e9eb0
Zengqi Wen
67a3262873bdaf626f1e9eb1
Jianhua Tao
Boosting Multimodal Reasoning with MCTS-Automated Structured Thinking
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.
AStar leverages MCTS to enhance multimodal large language models' visual reasoning from limited data, achieving superior accuracy while maintaining efficiency.
Multimodal large language models OpenAI o1 structured thinking explicit search structures teacher-guided distillation Monte Carlo Tree Search MCTS hierarchical structures unified reasoning framework internal reasoning capabilities external reasoning guidelines MathVerse benchmark GPT-4o