title: STAR-R1: Spatial TrAnsformation Reasoning by Reinforcing Multimodal LLMs
summary: Multimodal Large Language Models (MLLMs) have demonstrated remarkable
  capabilities across diverse tasks, yet they lag significantly behind humans in
  spatial reasoning. We investigate this gap through Transformation-Driven Visual
  Reasoning (TVR), a challenging task requiring identification of object
  transformations across images under varying viewpoints. While traditional
  Supervised Fine-Tuning (SFT) fails to generate coherent reasoning paths in
  cross-view settings, sparse-reward Reinforcement Learning (RL) suffers from
  inefficient exploration and slow convergence. To address these limitations, we
  propose STAR-R1, a novel framework that integrates a single-stage RL paradigm
  with a fine-grained reward mechanism tailored for TVR. Specifically, STAR-R1
  rewards partial correctness while penalizing excessive enumeration and passive
  inaction, enabling efficient exploration and precise reasoning. Comprehensive
  evaluations demonstrate that STAR-R1 achieves state-of-the-art performance
  across all 11 metrics, outperforming SFT by 23% in cross-view scenarios.
  Further analysis reveals STAR-R1's anthropomorphic behavior and highlights its
  unique ability to compare all objects for improving spatial reasoning. Our work
  provides critical insights in advancing the research of MLLMs and reasoning
  models. The codes, model weights, and data will be publicly available at
  https://github.com/zongzhao23/STAR-R1.
paper_authors._id: 6835adccc9704b79b7f18dd0
paper_authors.name: Zongzhao Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T12:49:37.146Z
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paper_authors.fullname: lzz
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paper_authors.user: lizongzhao
paper_authors._id: 6835adccc9704b79b7f18dd1
paper_authors.name: Zongyang Ma
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T12:49:26.103Z
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paper_authors.avatarUrl: /avatars/3b6eba701d64518d6f694942f5b2e9a9.svg
paper_authors.fullname: Zongyang Ma
paper_authors.type: user
paper_authors.user: zyma
paper_authors._id: 6835adccc9704b79b7f18dd2
paper_authors.name: Mingze Li
paper_authors._id: 6835adccc9704b79b7f18dd3
paper_authors.name: Songyou Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T12:49:52.377Z
paper_authors._id: 67d92d6061f369297a4a225a
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paper_authors.fullname: Songyou Li
paper_authors.type: user
paper_authors.user: Wthinker
paper_authors._id: 6835adccc9704b79b7f18dd4
paper_authors.name: Yu Rong
paper_authors._id: 6835adccc9704b79b7f18dd5
paper_authors.name: Tingyang Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T12:49:58.919Z
paper_authors._id: 67a5a25269f568c7eb4173cd
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paper_authors.fullname: Tingyang Xu
paper_authors.type: user
paper_authors.user: xuty007
paper_authors._id: 6835adccc9704b79b7f18dd6
paper_authors.name: Ziqi Zhang
paper_authors._id: 6835adccc9704b79b7f18dd7
paper_authors.name: Deli Zhao
paper_authors._id: 6835adccc9704b79b7f18dd8
paper_authors.name: Wenbing Huang
paper_title: STAR-R1: Spatial TrAnsformation Reasoning by Reinforcing Multimodal LLMs
paper_summary: Multimodal Large Language Models (MLLMs) have demonstrated remarkable
  capabilities across diverse tasks, yet they lag significantly behind humans in
  spatial reasoning. We investigate this gap through Transformation-Driven Visual
  Reasoning (TVR), a challenging task requiring identification of object
  transformations across images under varying viewpoints. While traditional
  Supervised Fine-Tuning (SFT) fails to generate coherent reasoning paths in
  cross-view settings, sparse-reward Reinforcement Learning (RL) suffers from
  inefficient exploration and slow convergence. To address these limitations, we
  propose STAR-R1, a novel framework that integrates a single-stage RL paradigm
  with a fine-grained reward mechanism tailored for TVR. Specifically, STAR-R1
  rewards partial correctness while penalizing excessive enumeration and passive
  inaction, enabling efficient exploration and precise reasoning. Comprehensive
  evaluations demonstrate that STAR-R1 achieves state-of-the-art performance
  across all 11 metrics, outperforming SFT by 23% in cross-view scenarios.
  Further analysis reveals STAR-R1's anthropomorphic behavior and highlights its
  unique ability to compare all objects for improving spatial reasoning. Our work
  provides critical insights in advancing the research of MLLMs and reasoning
  models. The codes, model weights, and data will be publicly available at
  https://github.com/zongzhao23/STAR-R1.
paper_ai_summary: STAR-R1, a novel RL framework with a fine-grained reward mechanism, enhances spatial reasoning in multimodal large language models by addressing limitations in traditional SFT and sparse-reward RL.
paper_ai_keywords: Transformation-Driven Visual Reasoning Supervised Fine-Tuning Reinforcement Learning single-stage RL fine-grained reward mechanism partial correctness excessive enumeration spatial reasoning multimodal large language models