title: OpenThinkIMG: Learning to Think with Images via Visual Tool
    Reinforcement Learning
summary: While humans can flexibly leverage interactive visual cognition for complex
  problem-solving, enabling Large Vision-Language Models (LVLMs) to learn
  similarly adaptive behaviors with visual tools remains challenging. A
  significant hurdle is the current lack of standardized infrastructure, which
  hinders integrating diverse tools, generating rich interaction data, and
  training robust agents effectively. To address these gaps, we introduce
  OpenThinkIMG, the first open-source, comprehensive end-to-end framework for
  tool-augmented LVLMs. It features standardized vision tool interfaces, scalable
  trajectory generation for policy initialization, and a flexible training
  environment. Furthermore, considering supervised fine-tuning (SFT) on static
  demonstrations offers limited policy generalization for dynamic tool
  invocation, we propose a novel reinforcement learning (RL) framework V-ToolRL
  to train LVLMs to learn adaptive policies for invoking external vision tools.
  V-ToolRL enables LVLMs to autonomously discover optimal tool-usage strategies
  by directly optimizing for task success using feedback from tool interactions.
  We empirically validate V-ToolRL on challenging chart reasoning tasks. Our
  RL-trained agent, built upon a Qwen2-VL-2B, significantly outperforms its
  SFT-initialized counterpart (+28.83 points) and surpasses established
  supervised tool-learning baselines like Taco and CogCom by an average of +12.7
  points. Notably, it also surpasses prominent closed-source models like GPT-4.1
  by +8.68 accuracy points. We hope OpenThinkIMG can serve as a foundational
  framework for advancing dynamic, tool-augmented visual reasoning, helping the
  community develop AI agents that can genuinely "think with images".
paper_authors._id: 6826aa068caf98415c50897f
paper_authors.name: Zhaochen Su
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paper_authors.name: Linjie Li
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paper_authors.fullname: Linjie Li
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paper_authors.name: Mingyang Song
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paper_authors.fullname: Mingyang Song
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paper_authors.user: hitsmy
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paper_authors.name: Yunzhuo Hao
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paper_authors.fullname: Yunzhuo Hao
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paper_authors.user: luckychao
paper_authors._id: 6826aa068caf98415c508983
paper_authors.name: Zhengyuan Yang
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paper_authors.avatarUrl: /avatars/fb36f69f03421c3a2a7f72ba0858fa60.svg
paper_authors.fullname: Zhengyuan Yang
paper_authors.type: user
paper_authors.user: zyang39
paper_authors._id: 6826aa068caf98415c508984
paper_authors.name: Jun Zhang
paper_authors._id: 6826aa068caf98415c508985
paper_authors.name: Guanjie Chen
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/2b839aad010bcd9364eed396e19f3700.svg
paper_authors.fullname: Guanjie Chen
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paper_authors.user: GuanjieChen
paper_authors._id: 6826aa068caf98415c508986
paper_authors.name: Jiawei Gu
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paper_authors.fullname: Jiawei Gu
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paper_authors.name: Juntao Li
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paper_authors.fullname: Juntao Li
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paper_authors.name: Xiaoye Qu
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paper_authors.fullname: Xiaoye Qu
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paper_authors.user: Xiaoye08
paper_authors._id: 6826aa068caf98415c508989
paper_authors.name: Yu Cheng
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paper_authors.avatarUrl: /avatars/997e1b9f54b27a7728a9d4abfee4ba91.svg
paper_authors.fullname: Yu Cheng
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paper_title: OpenThinkIMG: Learning to Think with Images via Visual Tool
    Reinforcement Learning
paper_summary: While humans can flexibly leverage interactive visual cognition for complex
  problem-solving, enabling Large Vision-Language Models (LVLMs) to learn
  similarly adaptive behaviors with visual tools remains challenging. A
  significant hurdle is the current lack of standardized infrastructure, which
  hinders integrating diverse tools, generating rich interaction data, and
  training robust agents effectively. To address these gaps, we introduce
  OpenThinkIMG, the first open-source, comprehensive end-to-end framework for
  tool-augmented LVLMs. It features standardized vision tool interfaces, scalable
  trajectory generation for policy initialization, and a flexible training
  environment. Furthermore, considering supervised fine-tuning (SFT) on static
  demonstrations offers limited policy generalization for dynamic tool
  invocation, we propose a novel reinforcement learning (RL) framework V-ToolRL
  to train LVLMs to learn adaptive policies for invoking external vision tools.
  V-ToolRL enables LVLMs to autonomously discover optimal tool-usage strategies
  by directly optimizing for task success using feedback from tool interactions.
  We empirically validate V-ToolRL on challenging chart reasoning tasks. Our
  RL-trained agent, built upon a Qwen2-VL-2B, significantly outperforms its
  SFT-initialized counterpart (+28.83 points) and surpasses established
  supervised tool-learning baselines like Taco and CogCom by an average of +12.7
  points. Notably, it also surpasses prominent closed-source models like GPT-4.1
  by +8.68 accuracy points. We hope OpenThinkIMG can serve as a foundational
  framework for advancing dynamic, tool-augmented visual reasoning, helping the
  community develop AI agents that can genuinely "think with images".
paper_ai_summary: OpenThinkIMG and V-ToolRL enable LVLMs to learn adaptive policies for using external vision tools, outperforming existing methods on chart reasoning tasks.
paper_ai_keywords: Large Vision-Language Models LVLMs OpenThinkIMG vision tool interfaces scalable trajectory generation reinforcement learning RL V-ToolRL adaptive policies task success Qwen2-VL-2B supervised fine-tuning SFT chart reasoning tasks Taco CogCom GPT-4.1