title: VLM-R^3: Region Recognition, Reasoning, and Refinement for Enhanced
    Multimodal Chain-of-Thought
summary: Recently, reasoning-based MLLMs have achieved a degree of success in
  generating long-form textual reasoning chains. However, they still struggle
  with complex tasks that necessitate dynamic and iterative focusing on and
  revisiting of visual regions to achieve precise grounding of textual reasoning
  in visual evidence. We introduce VLM-R^3 (Visual
  Language Model with Region Recognition and
  Reasoning), a framework that equips an MLLM with the ability to (i)
  decide when additional visual evidence is needed, (ii) determine
  where to ground within the image, and (iii) seamlessly weave the
  relevant sub-image content back into an interleaved chain-of-thought. The core
  of our method is Region-Conditioned Reinforcement Policy Optimization
  (R-GRPO), a training paradigm that rewards the model for selecting informative
  regions, formulating appropriate transformations (e.g.\ crop, zoom), and
  integrating the resulting visual context into subsequent reasoning steps. To
  bootstrap this policy, we compile a modest but carefully curated Visuo-Lingual
  Interleaved Rationale (VLIR) corpus that provides step-level supervision on
  region selection and textual justification. Extensive experiments on MathVista,
  ScienceQA, and other benchmarks show that VLM-R^3 sets a new state of the art
  in zero-shot and few-shot settings, with the largest gains appearing on
  questions demanding subtle spatial reasoning or fine-grained visual cue
  extraction.
paper_authors._id: 68302db3b73ef22aebdce9c2
paper_authors.name: Chaoya Jiang
paper_authors._id: 68302db3b73ef22aebdce9c3
paper_authors.name: Yongrui Heng
paper_authors._id: 68302db3b73ef22aebdce9c4
paper_authors.name: Wei Ye
paper_authors._id: 68302db3b73ef22aebdce9c5
paper_authors.name: Han Yang
paper_authors._id: 68302db3b73ef22aebdce9c6
paper_authors.name: Haiyang Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:14:45.417Z
paper_authors._id: 645b10e80c73ea27d13f7aca
paper_authors.avatarUrl: /avatars/95e565306472a15067440b5b43e07a6f.svg
paper_authors.fullname: xuhaiyang
paper_authors.type: user
paper_authors.user: xhyandwyy
paper_authors._id: 68302db3b73ef22aebdce9c7
paper_authors.name: Ming Yan
paper_authors._id: 68302db3b73ef22aebdce9c8
paper_authors.name: Ji Zhang
paper_authors._id: 68302db3b73ef22aebdce9c9
paper_authors.name: Fei Huang
paper_authors._id: 68302db3b73ef22aebdce9ca
paper_authors.name: Shikun Zhang
paper_title: VLM-R^3: Region Recognition, Reasoning, and Refinement for Enhanced
    Multimodal Chain-of-Thought
paper_summary: Recently, reasoning-based MLLMs have achieved a degree of success in
  generating long-form textual reasoning chains. However, they still struggle
  with complex tasks that necessitate dynamic and iterative focusing on and
  revisiting of visual regions to achieve precise grounding of textual reasoning
  in visual evidence. We introduce VLM-R^3 (Visual
  Language Model with Region Recognition and
  Reasoning), a framework that equips an MLLM with the ability to (i)
  decide when additional visual evidence is needed, (ii) determine
  where to ground within the image, and (iii) seamlessly weave the
  relevant sub-image content back into an interleaved chain-of-thought. The core
  of our method is Region-Conditioned Reinforcement Policy Optimization
  (R-GRPO), a training paradigm that rewards the model for selecting informative
  regions, formulating appropriate transformations (e.g.\ crop, zoom), and
  integrating the resulting visual context into subsequent reasoning steps. To
  bootstrap this policy, we compile a modest but carefully curated Visuo-Lingual
  Interleaved Rationale (VLIR) corpus that provides step-level supervision on
  region selection and textual justification. Extensive experiments on MathVista,
  ScienceQA, and other benchmarks show that VLM-R^3 sets a new state of the art
  in zero-shot and few-shot settings, with the largest gains appearing on
  questions demanding subtle spatial reasoning or fine-grained visual cue
  extraction.
paper_ai_summary: VLM-R3 enhances multi-modal language models with region recognition and reasoning, achieving state-of-the-art performance on visual question answering tasks through region-conditioned reinforcement policy optimization.
paper_ai_keywords: VLM-R3 Region-Conditioned Reinforcement Policy Optimization (R-GRPO) Visuo-Lingual Interleaved Rationale (VLIR) corpus MathVista ScienceQA