Vision-language reinforcement learning (RL) has primarily focused on narrow domains (e.g. geometry or chart reasoning). This leaves broader training scenarios and resources underexplored, limiting the exploration and learning of Vision Language Models (VLMs) through RL. We find video games inherently provide rich visual elements and mechanics that are easy to verify. To fully use the multimodal and verifiable reward in video games, we propose Game-RL, constructing diverse game tasks for RL training to boost VLMs general reasoning ability. To obtain training data, we propose Code2Logic, a novel approach that adapts game code to synthesize game reasoning task data, thus obtaining the GameQA dataset of 30 games and 158 tasks with controllable difficulty gradation. Unexpectedly, RL training solely on GameQA enables multiple VLMs to achieve performance improvements across 7 diverse vision-language benchmarks, demonstrating the value of Game-RL for enhancing VLMs' general reasoning. Furthermore, this suggests that video games may serve as valuable scenarios and resources to boost general reasoning abilities. Our code, dataset and models are available at the GitHub repository.
@article{arxiv.2505.13886,
title = {Game-RL: Synthesizing Multimodal Verifiable Game Data to Boost VLMs' General Reasoning},
author = {Jingqi Tong and Jixin Tang and Hangcheng Li and Yurong Mou and Ming Zhang and Jun Zhao and Yanbo Wen and Fan Song and Jiahao Zhan and Yuyang Lu and Chaoran Tao and Zhiyuan Guo and Jizhou Yu and Tianhao Cheng and Zhiheng Xi and Changhao Jiang and Zhangyue Yin and Yining Zheng and Weifeng Ge and Guanhua Chen and Tao Gui and Xipeng Qiu and Qi Zhang and Xuanjing Huang},
journal= {arXiv preprint arXiv:2505.13886},
year = {2025}
}