Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via
  Collective Monte Carlo Tree Search
Authors: name: Huanjin Yao, name: Jiaxing Huang, name: Wenhao Wu, name: Jingyi Zhang, name: Yibo Wang, name: Shunyu Liu, name: Yingjie Wang, name: Yuxin Song, name: Haocheng Feng, name: Li Shen, name: Dacheng Tao

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