title: Pixel Reasoner: Incentivizing Pixel-Space Reasoning with
    Curiosity-Driven Reinforcement Learning
summary: Chain-of-thought reasoning has significantly improved the performance of
  Large Language Models (LLMs) across various domains. However, this reasoning
  process has been confined exclusively to textual space, limiting its
  effectiveness in visually intensive tasks. To address this limitation, we
  introduce the concept of reasoning in the pixel-space. Within this novel
  framework, Vision-Language Models (VLMs) are equipped with a suite of visual
  reasoning operations, such as zoom-in and select-frame. These operations enable
  VLMs to directly inspect, interrogate, and infer from visual evidences, thereby
  enhancing reasoning fidelity for visual tasks. Cultivating such pixel-space
  reasoning capabilities in VLMs presents notable challenges, including the
  model's initially imbalanced competence and its reluctance to adopt the newly
  introduced pixel-space operations. We address these challenges through a
  two-phase training approach. The first phase employs instruction tuning on
  synthesized reasoning traces to familiarize the model with the novel visual
  operations. Following this, a reinforcement learning (RL) phase leverages a
  curiosity-driven reward scheme to balance exploration between pixel-space
  reasoning and textual reasoning. With these visual operations, VLMs can
  interact with complex visual inputs, such as information-rich images or videos
  to proactively gather necessary information. We demonstrate that this approach
  significantly improves VLM performance across diverse visual reasoning
  benchmarks. Our 7B model, \model, achieves 84\% on V* bench, 74\% on
  TallyQA-Complex, and 84\% on InfographicsVQA, marking the highest accuracy
  achieved by any open-source model to date. These results highlight the
  importance of pixel-space reasoning and the effectiveness of our framework.
paper_authors._id: 682fe6bd5f80e910085b5116
paper_authors.name: Alex Su
paper_authors._id: 682fe6bd5f80e910085b5117
paper_authors.name: Haozhe Wang
paper_authors._id: 682fe6bd5f80e910085b5118
paper_authors.name: Weimin Ren
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:42:24.885Z
paper_authors._id: 64405a9d518271b0d1beea38
paper_authors.avatarUrl: /avatars/b702474588fd7090773320422417a582.svg
paper_authors.fullname: Weiming Ren
paper_authors.type: user
paper_authors.user: wren93
paper_authors._id: 682fe6bd5f80e910085b5119
paper_authors.name: Fangzhen Lin
paper_authors._id: 682fe6bd5f80e910085b511a
paper_authors.name: Wenhu Chen
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-23T03:08:46.964Z
paper_authors._id: 6313a86154e6e5d9f0f94e04
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1662232951344-6313a86154e6e5d9f0f94e04.jpeg
paper_authors.fullname: Wenhu Chen
paper_authors.type: user
paper_authors.user: wenhu
paper_title: Pixel Reasoner: Incentivizing Pixel-Space Reasoning with
    Curiosity-Driven Reinforcement Learning
paper_summary: Chain-of-thought reasoning has significantly improved the performance of
  Large Language Models (LLMs) across various domains. However, this reasoning
  process has been confined exclusively to textual space, limiting its
  effectiveness in visually intensive tasks. To address this limitation, we
  introduce the concept of reasoning in the pixel-space. Within this novel
  framework, Vision-Language Models (VLMs) are equipped with a suite of visual
  reasoning operations, such as zoom-in and select-frame. These operations enable
  VLMs to directly inspect, interrogate, and infer from visual evidences, thereby
  enhancing reasoning fidelity for visual tasks. Cultivating such pixel-space
  reasoning capabilities in VLMs presents notable challenges, including the
  model's initially imbalanced competence and its reluctance to adopt the newly
  introduced pixel-space operations. We address these challenges through a
  two-phase training approach. The first phase employs instruction tuning on
  synthesized reasoning traces to familiarize the model with the novel visual
  operations. Following this, a reinforcement learning (RL) phase leverages a
  curiosity-driven reward scheme to balance exploration between pixel-space
  reasoning and textual reasoning. With these visual operations, VLMs can
  interact with complex visual inputs, such as information-rich images or videos
  to proactively gather necessary information. We demonstrate that this approach
  significantly improves VLM performance across diverse visual reasoning
  benchmarks. Our 7B model, \model, achieves 84\% on V* bench, 74\% on
  TallyQA-Complex, and 84\% on InfographicsVQA, marking the highest accuracy
  achieved by any open-source model to date. These results highlight the
  importance of pixel-space reasoning and the effectiveness of our framework.
paper_ai_summary: Introducing pixel-space reasoning in Vision-Language Models (VLMs) through visual operations like zoom-in and select-frame enhances their performance on visual tasks.
paper_ai_keywords: Vision-Language Models VLMs pixel-space reasoning visual reasoning operations zoom-in select-frame reinforcement learning RL curiosity-driven reward scheme V* bench TallyQA-Complex InfographicsVQA