Summary of Mulberry: Empowering Mllm with O1-like Reasoning and Reflection Via Collective Monte Carlo Tree Search, by Huanjin Yao et al.
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
by Huanjin Yao, Jiaxing Huang, Wenhao Wu, Jingyi Zhang, Yibo Wang, Shunyu Liu, Yingjie Wang, Yuxin Song, Haocheng Feng, Li Shen, Dacheng Tao
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Collective Monte Carlo Tree Search (CoMCTS) method learns to reason by creating intermediate steps until the final answer. It introduces collective learning into tree search for efficient reasoning-path searching and learning. CoMCTS leverages knowledge from multiple models to conjecture, search, and identify effective reasoning paths via four operations: Expansion, Simulation, Error Positioning, Backpropagation, and Selection. The method is applied to train Mulberry, a series of multimodal language models with step-by-step Reasoning and Reflection capabilities. Extensive experiments demonstrate the superiority of CoMCTS on various benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We developed a new way for machines to understand and answer questions by breaking down the thinking process into smaller steps. We call this method Collective Monte Carlo Tree Search (CoMCTS). It lets many models work together to find the best way to solve a problem. We tested CoMCTS on a big dataset called Mulberry-260k, which has lots of examples of how to reason step-by-step. Our results show that CoMCTS is better than other methods at answering questions. |
Keywords
» Artificial intelligence » Backpropagation
