title: SATORI-R1: Incentivizing Multimodal Reasoning with Spatial Grounding and
    Verifiable Rewards
summary: DeepSeek-R1 has demonstrated powerful reasoning capabilities in the text
  domain through stable reinforcement learning (RL). Recently, in the multimodal
  domain, works have begun to directly apply RL to generate R1-like free-form
  reasoning for Visual Question Answering (VQA) tasks. However, multimodal tasks
  share an intrinsically different nature from textual tasks, which heavily rely
  on the understanding of the input image to solve the problem. Therefore, such
  free-form reasoning faces two critical limitations in the VQA task: (1)
  Extended reasoning chains diffuse visual focus away from task-critical regions,
  degrading answer accuracy. (2) Unverifiable intermediate steps amplify
  policy-gradient variance and computational costs overhead. To address these
  issues, in this paper, we introduce SATORI (Spatially
  Anchored Task Optimization with
  ReInforcement Learning), which decomposes VQA into three
  verifiable stages, including global image captioning, region localization, and
  answer prediction, each supplying explicit reward signals. Furthermore, we also
  introduce VQA-Verify, a 12k dataset annotated with answer-aligned captions and
  bounding-boxes to facilitate training. Experiments demonstrate consistent
  performance improvements across seven VQA benchmarks, achieving up to 15.7%
  improvement in accuracy in accuracy compared to the R1-like baseline. Our
  analysis of the attention map confirms enhanced focus on critical regions,
  which brings improvements in accuracy. Our code is available at
  https://github.com/justairr/SATORI-R1.
paper_authors._id: 683524e492151d8beb27f7eb
paper_authors.name: Chuming Shen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T14:44:01.542Z
paper_authors._id: 64afb1759f6af3d885e79596
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64afb1759f6af3d885e79596/U0C_zSEhpY4HKRZbL3EYr.png
paper_authors.fullname: Chuming Shen
paper_authors.type: user
paper_authors.user: justairr
paper_authors._id: 683524e492151d8beb27f7ec
paper_authors.name: Wei Wei
paper_authors._id: 683524e492151d8beb27f7ed
paper_authors.name: Xiaoye Qu
paper_authors._id: 683524e492151d8beb27f7ee
paper_authors.name: Yu Cheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:49:07.836Z
paper_authors._id: 67017abfe4d49b157ac534d9
paper_authors.avatarUrl: /avatars/997e1b9f54b27a7728a9d4abfee4ba91.svg
paper_authors.fullname: Yu Cheng
paper_authors.type: user
paper_authors.user: ych133
paper_title: SATORI-R1: Incentivizing Multimodal Reasoning with Spatial Grounding and
    Verifiable Rewards
paper_summary: DeepSeek-R1 has demonstrated powerful reasoning capabilities in the text
  domain through stable reinforcement learning (RL). Recently, in the multimodal
  domain, works have begun to directly apply RL to generate R1-like free-form
  reasoning for Visual Question Answering (VQA) tasks. However, multimodal tasks
  share an intrinsically different nature from textual tasks, which heavily rely
  on the understanding of the input image to solve the problem. Therefore, such
  free-form reasoning faces two critical limitations in the VQA task: (1)
  Extended reasoning chains diffuse visual focus away from task-critical regions,
  degrading answer accuracy. (2) Unverifiable intermediate steps amplify
  policy-gradient variance and computational costs overhead. To address these
  issues, in this paper, we introduce SATORI (Spatially
  Anchored Task Optimization with
  ReInforcement Learning), which decomposes VQA into three
  verifiable stages, including global image captioning, region localization, and
  answer prediction, each supplying explicit reward signals. Furthermore, we also
  introduce VQA-Verify, a 12k dataset annotated with answer-aligned captions and
  bounding-boxes to facilitate training. Experiments demonstrate consistent
  performance improvements across seven VQA benchmarks, achieving up to 15.7%
  improvement in accuracy in accuracy compared to the R1-like baseline. Our
  analysis of the attention map confirms enhanced focus on critical regions,
  which brings improvements in accuracy. Our code is available at
  https://github.com/justairr/SATORI-R1.
paper_ai_summary: SATORI decomposes VQA into verifiable stages with explicit rewards to enhance focus on critical regions and reduce policy-gradient variance, achieving significant performance improvements.
paper_ai_keywords: stable reinforcement learning multimodal domain Visual Question Answering (VQA) extended reasoning chains policy-gradient variance global image captioning region localization answer prediction attention map VQA-Verify