Start Small, Think Big: Curriculum-based Relative Policy Optimization for Visual Grounding

• URL: http://arxiv.org/abs/2511.13924v1
• Date: Mon, 17 Nov 2025 21:22:50 GMT
• Title: Start Small, Think Big: Curriculum-based Relative Policy Optimization for Visual Grounding
• Authors: Qingyang Yan, Guangyao Chen, Yixiong Zou,
• Abstract summary: Chain-of-Thought (CoT) prompting has recently shown significant promise across various NLP and computer vision tasks.<n>We find that reinforcement learning (RL)-based fine-tuned CoT reasoning can paradoxically degrade performance in Visual Grounding tasks.<n>We propose Curriculum-based Relative Policy Optimization (CuRPO), a novel training strategy that leverages CoT length and generalized Intersection over Union rewards.
• Score: 23.138205646078536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chain-of-Thought (CoT) prompting has recently shown significant promise across various NLP and computer vision tasks by explicitly generating intermediate reasoning steps. However, we find that reinforcement learning (RL)-based fine-tuned CoT reasoning can paradoxically degrade performance in Visual Grounding tasks, particularly as CoT outputs become lengthy or complex. Additionally, our analysis reveals that increased dataset size does not always enhance performance due to varying data complexities. Motivated by these findings, we propose Curriculum-based Relative Policy Optimization (CuRPO), a novel training strategy that leverages CoT length and generalized Intersection over Union (gIoU) rewards as complexity indicators to progressively structure training data from simpler to more challenging examples. Extensive experiments on RefCOCO, RefCOCO+, RefCOCOg, and LISA datasets demonstrate the effectiveness of our approach. CuRPO consistently outperforms existing methods, including Visual-RFT, with notable improvements of up to +12.52 mAP on RefCOCO. Moreover, CuRPO exhibits exceptional efficiency and robustness, delivering strong localization performance even in few-shot learning scenarios, particularly benefiting tasks characterized by ambiguous and intricate textual descriptions.The code is released on https://github.com/qyoung-yan/CuRPO.

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