Game-RL: Synthesizing Multimodal Verifiable Game Data to Boost VLMs' General Reasoning

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 leverage the multimodal and verifiable rewards 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 reasoning data with unlimited examples and controllable difficulty gradation, thus obtaining the GameQA dataset of 30 games and 158 verifiable tasks. Remarkably, RL training solely on GameQA enables multiple VLMs to generalize across 7 diverse out-of-domain vision-language benchmarks, demonstrating the value of Game-RL for enhancing VLMs’ general reasoning. Furthermore, game data provides improvements comparable to general multimodal reasoning datasets (e.g. geometry/chart). More importantly, scaling up game diversity or game data volume consistently improves VLMs' generalizable reasoning capabilities. Our findings highlight scaling reinforcement learning in game environments as a promising direction for enhancing generalizable multimodal reasoning in foundation models. All code, dataset and model weights are released at \url{https://github.com/tongjingqi/Game-RL}.