title: Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via
    Collective Monte Carlo Tree Search
summary: 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
paper_authors._id: 676d843d92c4a8fe49532793
paper_authors.name: Huanjin Yao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-30T21:16:04.725Z
paper_authors._id: 6590e03454f8826173ed5ee6
paper_authors.avatarUrl: /avatars/b2fbaaf444e1e53c5e914cd42a41389a.svg
paper_authors.fullname: Huanjin Yao
paper_authors.type: user
paper_authors.user: HuanjinYao
paper_authors._id: 676d843d92c4a8fe49532794
paper_authors.name: Jiaxing Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-30T21:15:31.439Z
paper_authors._id: 65237910b80dc49ba03a96d9
paper_authors.avatarUrl: /avatars/9d81c4c8fb2d597079e8dd9d9b79a8d8.svg
paper_authors.fullname: jiaxing
paper_authors.type: user
paper_authors.user: huangjiaxing
paper_authors._id: 676d843d92c4a8fe49532795
paper_authors.name: Wenhao Wu
paper_authors._id: 676d843d92c4a8fe49532796
paper_authors.name: Jingyi Zhang
paper_authors._id: 676d843d92c4a8fe49532797
paper_authors.name: Yibo Wang
paper_authors._id: 676d843d92c4a8fe49532798
paper_authors.name: Shunyu Liu
paper_authors._id: 676d843d92c4a8fe49532799
paper_authors.name: Yingjie Wang
paper_authors._id: 676d843d92c4a8fe4953279a
paper_authors.name: Yuxin Song
paper_authors._id: 676d843d92c4a8fe4953279b
paper_authors.name: Haocheng Feng
paper_authors._id: 676d843d92c4a8fe4953279c
paper_authors.name: Li Shen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-26T11:38:47.131Z
paper_authors._id: 62de356ad89af8c07209e7d4
paper_authors.avatarUrl: /avatars/610629958726b270418368b8b7f61469.svg
paper_authors.fullname: Li Shen
paper_authors.type: user
paper_authors.user: mathshenli
paper_authors._id: 676d843d92c4a8fe4953279d
paper_authors.name: Dacheng Tao
paper_title: Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via
    Collective Monte Carlo Tree Search
paper_summary: 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
paper_ai_keywords: Multimodal Large Language Model (MLLM) Collective Monte Carlo Tree Search (CoMCTS) Learning-to-reason Collective learning Reasoning-path searching Reasoning paths Iterative operations Expansion Simulation and Error Positioning Backpropagation Selection Mulberry-260k Step-by-step Reasoning Reflection