title: DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models
summary: The recent explosive interest in the reasoning capabilities of large language
  models, such as DeepSeek-R1, has demonstrated remarkable success through
  reinforcement learning-based fine-tuning frameworks, exemplified by methods
  like Group Relative Policy Optimization (GRPO). However, such reasoning
  abilities remain underexplored and notably absent in vision foundation models,
  including representation models like the DINO series. In this work, we propose
  DINO-R1, the first such attempt to incentivize visual in-context
  reasoning capabilities of vision foundation models using reinforcement
  learning. Specifically, DINO-R1 introduces Group Relative Query
  Optimization (GRQO), a novel reinforcement-style training strategy explicitly
  designed for query-based representation models, which computes query-level
  rewards based on group-normalized alignment quality. We also apply
  KL-regularization to stabilize the objectness distribution to reduce the
  training instability. This joint optimization enables dense and expressive
  supervision across queries while mitigating overfitting and distributional
  drift. Building upon Grounding-DINO, we train a series of DINO-R1 family models
  that integrate a visual prompt encoder and a visual-guided query selection
  mechanism. Extensive experiments on COCO, LVIS, and ODinW demonstrate that
  DINO-R1 significantly outperforms supervised fine-tuning baselines, achieving
  strong generalization in both open-vocabulary and closed-set visual prompting
  scenarios.
paper_authors._id: 683dee0d6416d41bef4739d4
paper_authors.name: Chenbin Pan
paper_authors._id: 683dee0d6416d41bef4739d5
paper_authors.name: Wenbin He
paper_authors._id: 683dee0d6416d41bef4739d6
paper_authors.name: Zhengzhong Tu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:47:37.512Z
paper_authors._id: 62548d5fef3debb2ddf91217
paper_authors.avatarUrl: /avatars/14975b45568f9c399c92c3986b6ce83e.svg
paper_authors.fullname: Zhengzhong Tu
paper_authors.type: user
paper_authors.user: vztu
paper_authors._id: 683dee0d6416d41bef4739d7
paper_authors.name: Liu Ren
paper_title: DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models
paper_summary: The recent explosive interest in the reasoning capabilities of large language
  models, such as DeepSeek-R1, has demonstrated remarkable success through
  reinforcement learning-based fine-tuning frameworks, exemplified by methods
  like Group Relative Policy Optimization (GRPO). However, such reasoning
  abilities remain underexplored and notably absent in vision foundation models,
  including representation models like the DINO series. In this work, we propose
  DINO-R1, the first such attempt to incentivize visual in-context
  reasoning capabilities of vision foundation models using reinforcement
  learning. Specifically, DINO-R1 introduces Group Relative Query
  Optimization (GRQO), a novel reinforcement-style training strategy explicitly
  designed for query-based representation models, which computes query-level
  rewards based on group-normalized alignment quality. We also apply
  KL-regularization to stabilize the objectness distribution to reduce the
  training instability. This joint optimization enables dense and expressive
  supervision across queries while mitigating overfitting and distributional
  drift. Building upon Grounding-DINO, we train a series of DINO-R1 family models
  that integrate a visual prompt encoder and a visual-guided query selection
  mechanism. Extensive experiments on COCO, LVIS, and ODinW demonstrate that
  DINO-R1 significantly outperforms supervised fine-tuning baselines, achieving
  strong generalization in both open-vocabulary and closed-set visual prompting
  scenarios.
paper_ai_summary: DINO-R1 incorporates reinforcement learning to enhance visual in-context reasoning capabilities in vision foundation models, achieving better performance than supervised fine-tuning across various visual prompting scenarios.
paper_ai_keywords: DeepSeek-R1 reinforcement learning-based fine-tuning Group Relative Policy Optimization (GRPO) visual in-context reasoning vision foundation models DINO series DINO-R1 Group Relative Query Optimization (GRQO) KL-regularization objectness distribution Grounding-DINO visual prompt encoder visual-guided query selection mechanism COCO LVIS ODinW open-vocabulary closed-set visual prompting