Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
FreeEmpowering MLLM with o1-like Reasoning and Reflection via CoMCTS
About Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
Mulberry is a series of Multimodal Large Language Models (MLLMs) enhanced with o1-like step-by-step reasoning and reflection capabilities, developed using Collective Monte Carlo Tree Search (CoMCTS). CoMCTS is a novel learning-to-reason method that leverages collective knowledge from multiple models to collaboratively search and identify effective reasoning paths through iterative expansion, simulation with error positioning, backpropagation, and selection. The method is used to construct the Mulberry-260k dataset, a multimodal dataset with a tree of well-defined reasoning nodes. Mulberry models demonstrate superior performance on various multimodal reasoning benchmarks, as detailed in a technical report on arXiv.
Key Features
Pros & Cons
- Introduces a novel collective learning approach for reasoning-path search
- Provides explicit and well-defined reasoning steps in dataset
- Demonstrates superiority on various multimodal benchmarks
- Open source with code to be released
- As a research project, practical deployment readiness may be limited
- Requires access to multiple models for collective learning
- Performance on real-world, diverse multimodal tasks may need further validation