Code2Logic: Game-Code-Driven Data Synthesis for Enhancing VLMs General Reasoning

• URL: http://arxiv.org/abs/2505.13886v3
• Date: Thu, 17 Jul 2025 08:46:29 GMT
• Title: Code2Logic: Game-Code-Driven Data Synthesis for Enhancing VLMs General Reasoning
• Authors: Jingqi Tong, Jixin Tang, Hangcheng Li, Yurong Mou, Ming Zhang, Jun Zhao, Yanbo Wen, Fan Song, Jiahao Zhan, Yuyang Lu, Chaoran Tao, Zhiyuan Guo, Jizhou Yu, Tianhao Cheng, Changhao Jiang, Zhen Wang, Tao Liang, Zhihui Fei, Mingyang Wan, Guojun Ma, Weifeng Ge, Guanhua Chen, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang,
• Abstract summary: We propose Code2Logic, a novel game-code-driven approach for multimodal reasoning data synthesis.<n>Our approach leverages Large Language Models (LLMs) to adapt game code, enabling automatic acquisition of reasoning processes and results through code execution.
• Score: 69.7882311630412
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual-language Chain-of-Thought (CoT) data resources are relatively scarce compared to text-only counterparts, limiting the improvement of reasoning capabilities in Vision Language Models (VLMs). However, high-quality vision-language reasoning data is expensive and labor-intensive to annotate. To address this issue, we leverage a promising resource: game code, which naturally contains logical structures and state transition processes. Therefore, we propose Code2Logic, a novel game-code-driven approach for multimodal reasoning data synthesis. Our approach leverages Large Language Models (LLMs) to adapt game code, enabling automatic acquisition of reasoning processes and results through code execution. Using the Code2Logic approach, we developed the GameQA dataset to train and evaluate VLMs. GameQA is cost-effective and scalable, offers controllable difficulty gradation and is diverse with 30 games and 158 tasks. Surprisingly, despite training solely on game data, VLMs demonstrated out of domain generalization, specifically Qwen2.5-VL-7B improving performance by 2.33% across 7 diverse vision-language benchmarks. Our code, dataset and models are available at https://github.com/tongjingqi/Code2Logic.

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