MathCoder-VL: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning

• URL: http://arxiv.org/abs/2505.10557v1
• Date: Thu, 15 May 2025 17:59:21 GMT
• Title: MathCoder-VL: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning
• Authors: Ke Wang, Junting Pan, Linda Wei, Aojun Zhou, Weikang Shi, Zimu Lu, Han Xiao, Yunqiao Yang, Houxing Ren, Mingjie Zhan, Hongsheng Li,
• Abstract summary: We propose leveraging code as supervision for cross-modal alignment, since code inherently encodes all information needed to generate corresponding figures.<n>Specifically, we co-develop our image-to-code model and dataset with model-in-the-loop approach.<n>We present MathCoder-VL, trained with ImgCode-8.6M for cross-modal alignment and subsequently fine-tuned on MM-MathInstruct-3M for multimodal math problem solving.
• Score: 36.55610944179401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural language image-caption datasets, widely used for training Large Multimodal Models, mainly focus on natural scenarios and overlook the intricate details of mathematical figures that are critical for problem-solving, hindering the advancement of current LMMs in multimodal mathematical reasoning. To this end, we propose leveraging code as supervision for cross-modal alignment, since code inherently encodes all information needed to generate corresponding figures, establishing a precise connection between the two modalities. Specifically, we co-develop our image-to-code model and dataset with model-in-the-loop approach, resulting in an image-to-code model, FigCodifier and ImgCode-8.6M dataset, the largest image-code dataset to date. Furthermore, we utilize FigCodifier to synthesize novel mathematical figures and then construct MM-MathInstruct-3M, a high-quality multimodal math instruction fine-tuning dataset. Finally, we present MathCoder-VL, trained with ImgCode-8.6M for cross-modal alignment and subsequently fine-tuned on MM-MathInstruct-3M for multimodal math problem solving. Our model achieves a new open-source SOTA across all six metrics. Notably, it surpasses GPT-4o and Claude 3.5 Sonnet in the geometry problem-solving subset of MathVista, achieving improvements of 8.9% and 9.2%. The dataset and models will be released at https://github.com/mathllm/MathCoder.

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