Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via
  Collective Monte Carlo Tree Search
In this work, we aim to develop an MLLM that understands and solves questions
by learning to create each intermediate step of the reasoning involved till the
final answer. To this end, we propose Collective Monte Carlo Tree Search
(CoMCTS), a new learning-to-reason method for MLLMs, which introduces the
concept of collective learning into ``tree search'' for effective and efficient
reasoning-path searching and learning. The core idea of CoMCTS is to leverage
collective knowledge from multiple models to collaboratively conjecture, search
and identify effective reasoning paths toward correct answers via four
iterative operations including Expansion, Simulation and Error Positioning,
Backpropagation, and Selection. Using CoMCTS, we construct Mulberry-260k, a
multimodal dataset with a tree of rich, explicit and well-defined reasoning
nodes for each question. With Mulberry-260k, we perform collective SFT to train
our model, Mulberry, a series of MLLMs with o1-like step-by-step Reasoning and
Reflection capabilities. Extensive experiments demonstrate the superiority of
our proposed methods on various benchmarks. Code will be available at
https://github.com/HJYao00/Mulberry
676d843d92c4a8fe49532793
Huanjin Yao
admin_assigned
2024-12-30T21:16:04.725Z
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/avatars/f5e59d3e58c28a99f2ff39267ca51cdb.svg
Huanjin Yao
user
HuanjinYao
676d843d92c4a8fe49532794
Jiaxing Huang
admin_assigned
2024-12-30T21:15:31.439Z
65237910b80dc49ba03a96d9
/avatars/9d81c4c8fb2d597079e8dd9d9b79a8d8.svg
jiaxing
user
huangjiaxing
676d843d92c4a8fe49532795
Wenhao Wu
676d843d92c4a8fe49532796
Jingyi Zhang
claimed_verified
2025-03-24T08:55:56.935Z
67d7d1bc1414fcb61968daed
/avatars/df3d10511a73ab6370d6df90f2759489.svg
Jingyi Zhang
user
jingyiZ00
676d843d92c4a8fe49532797
Yibo Wang
676d843d92c4a8fe49532798
Shunyu Liu
676d843d92c4a8fe49532799
Yingjie Wang
676d843d92c4a8fe4953279a
Yuxin Song
676d843d92c4a8fe4953279b
Haocheng Feng
676d843d92c4a8fe4953279c
Li Shen
claimed_verified
2025-01-26T11:38:47.131Z
62de356ad89af8c07209e7d4
/avatars/610629958726b270418368b8b7f61469.svg
Li Shen
user
mathshenli
676d843d92c4a8fe4953279d
Dacheng Tao
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via
  Collective Monte Carlo Tree Search
In this work, we aim to develop an MLLM that understands and solves questions
by learning to create each intermediate step of the reasoning involved till the
final answer. To this end, we propose Collective Monte Carlo Tree Search
(CoMCTS), a new learning-to-reason method for MLLMs, which introduces the
concept of collective learning into ``tree search'' for effective and efficient
reasoning-path searching and learning. The core idea of CoMCTS is to leverage
collective knowledge from multiple models to collaboratively conjecture, search
and identify effective reasoning paths toward correct answers via four
iterative operations including Expansion, Simulation and Error Positioning,
Backpropagation, and Selection. Using CoMCTS, we construct Mulberry-260k, a
multimodal dataset with a tree of rich, explicit and well-defined reasoning
nodes for each question. With Mulberry-260k, we perform collective SFT to train
our model, Mulberry, a series of MLLMs with o1-like step-by-step Reasoning and
Reflection capabilities. Extensive experiments demonstrate the superiority of
our proposed methods on various benchmarks. Code will be available at
https://github.com/HJYao00/Mulberry
A new learning-to-reason method called Collective Monte Carlo Tree Search (CoMCTS) enhances the reasoning capabilities of MLLMs by leveraging collective knowledge, demonstrated through experiments on various benchmarks with Mulberry-260k dataset and Mulberry model.
MLLM Collective Monte Carlo Tree Search CoMCTS tree search collective learning reasoning-path searching reasoning nodes Mulberry-260k collective SFT step-by-step Reasoning Reflection capabilities