Seventy3

Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。

今天的主题是：

Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search

Summary

This research paper introduces Mulberry, a series of multimodal large language models (MLLMs) designed for improved reasoning and reflection. The key innovation is CoMCTS, a novel Collective Monte Carlo Tree Search method that leverages multiple models to collaboratively identify effective reasoning paths. CoMCTS generates the Mulberry-260k dataset, featuring richly annotated reasoning trees for diverse multimodal questions. Extensive experiments demonstrate Mulberry's superior performance on various benchmarks compared to existing MLLMs. The paper concludes by highlighting CoMCTS and Mulberry-260k as valuable resources for future research in MLLM reasoning.

本文提出了Mulberry，一系列多模态大型语言模型（MLLMs），旨在提升推理和反思能力。其核心创新是CoMCTS（集体蒙特卡罗树搜索），一种新型方法，利用多个模型协作识别有效的推理路径。CoMCTS 生成了 Mulberry-260k 数据集，其中包含针对多样化多模态问题的丰富注释推理树。大量实验表明，Mulberry 在多个基准测试上的性能优于现有的多模态语言模型。论文总结指出，CoMCTS 和 Mulberry-260k 是未来多模态语言模型推理研究的宝贵资源。

原文链接：https://arxiv.org/abs/2412.18319