G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning
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arXiv
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| Format: | Preprint |
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2025
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| author | Chen, Liang Gao, Hongcheng Liu, Tianyu Huang, Zhiqi Sung, Flood Zhou, Xinyu Wu, Yuxin Chang, Baobao |
| author_facet | Chen, Liang Gao, Hongcheng Liu, Tianyu Huang, Zhiqi Sung, Flood Zhou, Xinyu Wu, Yuxin Chang, Baobao |
| contents | Vision-Language Models (VLMs) excel in many direct multimodal tasks but struggle to translate this prowess into effective decision-making within interactive, visually rich environments like games. This ``knowing-doing'' gap significantly limits their potential as autonomous agents, as leading VLMs often performing badly in simple games. To address this, we introduce VLM-Gym, a curated reinforcement learning (RL) environment featuring diverse visual games with unified interfaces and adjustable, compositional difficulty, specifically designed for scalable multi-game parallel training. Leveraging VLM-Gym, we train G0 models using pure RL-driven self-evolution, which demonstrate emergent perception and reasoning patterns. To further mitigate challenges arising from game diversity, we develop G1 models. G1 incorporates a perception-enhanced cold start prior to RL fine-tuning. Our resulting G1 models consistently surpass their teacher across all games and outperform leading proprietary models like Claude-3.7-Sonnet-Thinking. Systematic analysis reveals an intriguing finding: perception and reasoning abilities mutually bootstrap each other throughout the RL training process. Source code including VLM-Gym and RL training are released at https://github.com/chenllliang/G1 to foster future research in advancing VLMs as capable interactive agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13426 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Chen, Liang Gao, Hongcheng Liu, Tianyu Huang, Zhiqi Sung, Flood Zhou, Xinyu Wu, Yuxin Chang, Baobao Computer Vision and Pattern Recognition Vision-Language Models (VLMs) excel in many direct multimodal tasks but struggle to translate this prowess into effective decision-making within interactive, visually rich environments like games. This ``knowing-doing'' gap significantly limits their potential as autonomous agents, as leading VLMs often performing badly in simple games. To address this, we introduce VLM-Gym, a curated reinforcement learning (RL) environment featuring diverse visual games with unified interfaces and adjustable, compositional difficulty, specifically designed for scalable multi-game parallel training. Leveraging VLM-Gym, we train G0 models using pure RL-driven self-evolution, which demonstrate emergent perception and reasoning patterns. To further mitigate challenges arising from game diversity, we develop G1 models. G1 incorporates a perception-enhanced cold start prior to RL fine-tuning. Our resulting G1 models consistently surpass their teacher across all games and outperform leading proprietary models like Claude-3.7-Sonnet-Thinking. Systematic analysis reveals an intriguing finding: perception and reasoning abilities mutually bootstrap each other throughout the RL training process. Source code including VLM-Gym and RL training are released at https://github.com/chenllliang/G1 to foster future research in advancing VLMs as capable interactive agents. |
| title | G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.13426 |