G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning

• URL: http://arxiv.org/abs/2505.13426v1
• Date: Mon, 19 May 2025 17:54:39 GMT
• Title: G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning
• Authors: Liang Chen, Hongcheng Gao, Tianyu Liu, Zhiqi Huang, Flood Sung, Xinyu Zhou, Yuxin Wu, Baobao Chang,
• Abstract 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.<n>We introduce VLM-Gym, a curated reinforcement learning environment featuring diverse visual games with unified interfaces and adjustable, compositional difficulty.<n>We train G0 models using pure RL-driven self-evolution, which demonstrate emergent perception and reasoning patterns.<n>Our resulting G1 models consistently surpass their teacher across all games and outperform leading proprietary models like Claude-3.7-Sonnet-Thinking.
• Score: 37.18982308118744
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

Related papers

• Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for Visual Reasoning [60.292578064172524]
  We introduce a two-stage paradigm built on Qwen2.5-VL-7B: a massive linguistic cold-start fine-tuning, followed by multimodal reinforcement learning.<n>This pioneering work reveals three fundamental insights: 1) Behavior transfer emerges surprisingly early in cold start due to linguistic mental imagery, 2) Cold start broadly memorizes visual behaviors, while RL critically discerns and scales up effective patterns.<n>Our resulting model, Open-Vision-Reasoner (OVR), achieves state-of-the-art performance on a suite of reasoning benchmarks, including 95.3% on MATH500, 51.8% on MathVision and 54.6%
  arXiv  Detail & Related papers  (2025-07-07T17:59:03Z)
• Play to Generalize: Learning to Reason Through Game Play [11.778612579151067]
  We propose a novel post-training paradigm, Visual Game Learning, where MLLMs develop out-of-domain generalization of multimodal reasoning through playing arcade-like games.<n>Our findings suggest a new post-training paradigm: synthetic, rule-based games can serve as controllable and scalable pre-text tasks.
  arXiv  Detail & Related papers  (2025-06-09T17:59:57Z)
• Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning [28.92744927199283]
  ReVisual-R1 achieves a new state-of-the-art among open-source 7B MLLMs on challenging benchmarks including MathVerse, MathVision, WeMath, LogicVista, DynaMath, and challenging AIME2024 and AIME2025.
  arXiv  Detail & Related papers  (2025-06-04T17:51:08Z)
• Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games [36.162843233798455]
  Large language models (LLMs) have been observed to suddenly exhibit advanced reasoning abilities during reinforcement learning (RL)<n>We propose Divide-Fuse-Conquer, a framework designed to enhance generalization in multi-scenario RL.
  arXiv  Detail & Related papers  (2025-05-22T08:52:21Z)
• OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning [57.89304342666846]
  We introduce OpenThinkIMG, the first open-source, comprehensive end-to-end framework for tool-augmented LVLMs.<n>We propose a novel reinforcement learning framework V-ToolRL to train LVLMs to learn adaptive policies for invoking external vision tools.<n>V-ToolRL enables LVLMs to autonomously discover optimal tool-usage strategies.
  arXiv  Detail & Related papers  (2025-05-13T14:35:51Z)
• VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning [55.97950660659051]
  We aim to enhance the slow-thinking capabilities of vision-language models using reinforcement learning (without relying on distillation)<n>We introduce Forced Rethinking, which appends a rethinking trigger token to the end of rollouts in RL training, explicitly enforcing a self-reflection reasoning step.<n>Our model, VL-Rethinker, advances state-of-the-art scores on MathVista, MathVerse to achieve 80.4%, 63.5% respectively.
  arXiv  Detail & Related papers  (2025-04-10T17:41:56Z)
• VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model [29.524164786422368]
  Recently, DeepSeek R1 has shown that reinforcement learning can substantially improve the reasoning capabilities of Large Language Models (LLMs)<n>We investigate the extension of R1-style reinforcement learning to Vision-Language Models (VLMs)<n>We develop VLM-R1, a dedicated framework designed to harness RL for improving VLMs' performance on general vision-language tasks.
  arXiv  Detail & Related papers  (2025-04-10T10:05:15Z)
• Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1 [53.894789613838654]
  We introduce SEED-Bench-R1, a benchmark designed to evaluate post-training methods for MLLMs in video understanding.<n>It includes intricate real-world videos and complex everyday planning tasks in the format of multiple-choice questions.<n>Using Qwen2-VL-Instruct-7B as a base model, we compare RL with supervised fine-tuning (SFT)<n>Our detailed analysis reveals that RL enhances visual perception but often produces less coherent reasoning chains.
  arXiv  Detail & Related papers  (2025-03-31T17:55:23Z)
• Cultivating Game Sense for Yourself: Making VLMs Gaming Experts [23.370716496046217]
  We propose a paradigm shift in gameplay agent design.<n>Instead of directly controlling gameplay, VLM develops specialized execution modules tailored for tasks like shooting and combat.<n>These modules handle real-time game interactions, elevating VLM to a high-level developer.
  arXiv  Detail & Related papers  (2025-03-27T08:40:47Z)
• OpenVLThinker: An Early Exploration to Complex Vision-Language Reasoning via Iterative Self-Improvement [91.88062410741833]
  This study investigates whether similar reasoning capabilities can be successfully integrated into large vision-language models (LVLMs)<n>We consider an approach that iteratively leverages supervised fine-tuning (SFT) on lightweight training data and Reinforcement Learning (RL) to further improve model generalization.<n>OpenVLThinker, a LVLM exhibiting consistently improved reasoning performance on challenging benchmarks such as MathVista, MathVerse, and MathVision, demonstrates the potential of our strategy for robust vision-language reasoning.
  arXiv  Detail & Related papers  (2025-03-21T17:52:43Z)
• MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning [55.82649731348012]
  We introduce the MMK12 dataset and MM-EUREKA with 7B and 32B parameters.<n>The former is a high-quality multimodal mathematics reasoning dataset featuring diverse knowledge domains with human-verified answers and solution processes.<n>The latter is a multimodal model employing rule-based reinforcement learning utilizing online filtering and two-stage training strategy to enhance training stability.
  arXiv  Detail & Related papers  (2025-03-10T14:23:12Z)
• VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving [1.3107174618549584]
  Reinforcement learning (RL)-based methods for learning driving policies have gained increasing attention in the autonomous driving community.<n>Traditional RL approaches rely on manually engineered rewards, which require extensive human effort and often lack generalizability.<n>We propose textbfVLM-RL, a unified framework that integrates pre-trained Vision-Language Models (VLMs) with RL to generate reward signals.
  arXiv  Detail & Related papers  (2024-12-20T04:08:11Z)
• Vision-Language Models as a Source of Rewards [68.52824755339806]
  We investigate the feasibility of using off-the-shelf vision-language models, or VLMs, as sources of rewards for reinforcement learning agents. We show how rewards for visual achievement of a variety of language goals can be derived from the CLIP family of models, and used to train RL agents that can achieve a variety of language goals.
  arXiv  Detail & Related papers  (2023-12-14T18:06:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.