Actor as Its Own Critic: Unifying Region Understanding and Localization via CycleGRPO
This paper introduces Actor as Its Own Critic, a unified reinforcement learning framework, Cycle Group Relative Policy Optimization (CycleGRPO), that jointly optimizes region understanding and localization for Multimodal Large Language Models (MLLMs). Unlike existing separate pipelines, we leverage the inherent duality between the two tasks to construct a self-evaluating reinforcement learning paradigm: "region $\to$ text $\to$ region''. Specifically, a single MLLM first acts as the actor to generate region captions, then immediately transitions to a critic to ground its generated text back in the spatial domain. Therefore, CycleGRPO requires only region inputs, e.g., masks or bounding bo...
| Authors | Xin Zhang, Haochen Wang, Yikang Zhou et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 July 2026 |
| Last updated at source | 2 September 2026 |
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