DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models
Authors: name: Chenbin Pan, name: Wenbin He, name: Zhengzhong Tu, name: Liu Ren

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.