title: G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language
    Model via Reinforcement Learning
summary: 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.
paper_authors._id: 682c641925f124206513d14d
paper_authors.name: Liang Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T13:03:52.706Z
paper_authors._id: 61b0a4ce1b3d95b3d1ed9251
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Wwjr26vdudX5KYVTb8Q0a.png
paper_authors.fullname: Liang Chen
paper_authors.type: user
paper_authors.user: leonardPKU
paper_authors._id: 682c641925f124206513d14e
paper_authors.name: Hongcheng Gao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T13:04:07.611Z
paper_authors._id: 62728f4f6253fe2068da1021
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62728f4f6253fe2068da1021/KZ65X0EH98AF3zXemPiap.jpeg
paper_authors.fullname: Hongcheng Gao
paper_authors.type: user
paper_authors.user: HongchengGao
paper_authors._id: 682c641925f124206513d14f
paper_authors.name: Tianyu Liu
paper_authors._id: 682c641925f124206513d150
paper_authors.name: Zhiqi Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T13:04:23.439Z
paper_authors._id: 66221f1a90f3fd333c4ec52e
paper_authors.avatarUrl: /avatars/a3173d9603a69020ec24170831c97c2f.svg
paper_authors.fullname: Zhiqi Huang
paper_authors.type: user
paper_authors.user: Angelalilyer
paper_authors._id: 682c641925f124206513d151
paper_authors.name: Flood Sung
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T13:04:29.419Z
paper_authors._id: 6343d01a08c017b2c042305d
paper_authors.avatarUrl: /avatars/790c4104d80da9887d481f9efb494d81.svg
paper_authors.fullname: Flood Sung
paper_authors.type: user
paper_authors.user: floodsung
paper_authors._id: 682c641925f124206513d152
paper_authors.name: Xinyu Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T13:04:35.487Z
paper_authors._id: 6465adc302a171e703b3f093
paper_authors.avatarUrl: /avatars/639bdb4bc382633e40ec7ec78e6a2509.svg
paper_authors.fullname: Xinyu Zhou
paper_authors.type: user
paper_authors.user: xinyuzhou
paper_authors._id: 682c641925f124206513d153
paper_authors.name: Yuxin Wu
paper_authors._id: 682c641925f124206513d154
paper_authors.name: Baobao Chang
paper_title: G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language
    Model via Reinforcement Learning
paper_summary: 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.
paper_ai_summary: VLM-Gym addresses the "knowing-doing" gap in Vision-Language Models by training them in a diverse RL environment, leading to enhanced perception and reasoning abilities that surpass existing models in interactive games.
paper_ai_keywords: Vision-Language Models VLM-Gym reinforcement learning RL G0 models self-evolution G1 models perception-enhanced cold start RL fine-tuning