G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language
  Model via Reinforcement Learning
Authors: name: Liang Chen, name: Hongcheng Gao, name: Tianyu Liu, name: Zhiqi Huang, name: Flood Sung, name: Xinyu Zhou, name: Yuxin Wu, name: Baobao Chang
Organization: Moonshot AI

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.