AtomThink: A Slow Thinking Framework for Multimodal Mathematical Reasoning

• URL: http://arxiv.org/abs/2411.11930v2
• Date: Fri, 22 Nov 2024 03:24:15 GMT
• Title: AtomThink: A Slow Thinking Framework for Multimodal Mathematical Reasoning
• Authors: Kun Xiang, Zhili Liu, Zihao Jiang, Yunshuang Nie, Runhui Huang, Haoxiang Fan, Hanhui Li, Weiran Huang, Yihan Zeng, Jianhua Han, Lanqing Hong, Hang Xu, Xiaodan Liang,
• Abstract summary: AtomThink is a framework for constructing long chains of thought (CoT) in a step-by-step manner, guiding MLLMs to perform complex reasoning. AtomMATH is a large-scale multimodal dataset of long CoTs, and an atomic capability evaluation metric for mathematical tasks. AtomThink significantly improves the performance of baseline MLLMs, achieving approximately 50% relative accuracy gains on MathVista and 120% on MathVerse.
• Score: 70.95645743670062
• License:
• Abstract: In this paper, we address the challenging task of multimodal mathematical reasoning by incorporating the ability of ``slow thinking" into multimodal large language models (MLLMs). Contrary to existing methods that rely on direct or fast thinking, our key idea is to construct long chains of thought (CoT) consisting of atomic actions in a step-by-step manner, guiding MLLMs to perform complex reasoning. To this end, we design a novel AtomThink framework composed of three key modules: (i) a CoT annotation engine that automatically generates high-quality CoT annotations to address the lack of high-quality visual mathematical data; (ii) an atomic step fine-tuning strategy that jointly optimizes an MLLM and a policy reward model (PRM) for step-wise reasoning; and (iii) four different search strategies that can be applied with the PRM to complete reasoning. Additionally, we propose AtomMATH, a large-scale multimodal dataset of long CoTs, and an atomic capability evaluation metric for mathematical tasks. Extensive experimental results show that the proposed AtomThink significantly improves the performance of baseline MLLMs, achieving approximately 50\% relative accuracy gains on MathVista and 120\% on MathVerse. To support the advancement of multimodal slow-thinking models, we will make our code and dataset publicly available on https://github.com/Quinn777/AtomThink.

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