The Role of Entropy in Visual Grounding: Analysis and Optimization
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918236006645760 |
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| author | Li, Shuo Sun, Jiajun Zhang, Zhihao Fan, Xiaoran Jin, Senjie Li, Hui Yang, Yuming Ye, Junjie Shen, Lixing Ji, Tao Gui, Tao Zhang, Qi Huang, Xuanjing |
| author_facet | Li, Shuo Sun, Jiajun Zhang, Zhihao Fan, Xiaoran Jin, Senjie Li, Hui Yang, Yuming Ye, Junjie Shen, Lixing Ji, Tao Gui, Tao Zhang, Qi Huang, Xuanjing |
| contents | Recent advances in fine-tuning multimodal large language models (MLLMs) using reinforcement learning have achieved remarkable progress, particularly with the introduction of various entropy control techniques. However, the role and characteristics of entropy in perception-oriented tasks like visual grounding, as well as effective strategies for controlling it, remain largely unexplored. To address this issue, we focus on the visual grounding task and analyze the role and characteristics of entropy in comparison to reasoning tasks. Building on these findings, we introduce ECVGPO (Entropy Control Visual Grounding Policy Optimization), an interpretable algorithm designed for effective entropy regulation. Through entropy control, the trade-off between exploration and exploitation is better balanced. Experiments show that ECVGPO achieves broad improvements across various benchmarks and models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06726 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Role of Entropy in Visual Grounding: Analysis and Optimization Li, Shuo Sun, Jiajun Zhang, Zhihao Fan, Xiaoran Jin, Senjie Li, Hui Yang, Yuming Ye, Junjie Shen, Lixing Ji, Tao Gui, Tao Zhang, Qi Huang, Xuanjing Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Recent advances in fine-tuning multimodal large language models (MLLMs) using reinforcement learning have achieved remarkable progress, particularly with the introduction of various entropy control techniques. However, the role and characteristics of entropy in perception-oriented tasks like visual grounding, as well as effective strategies for controlling it, remain largely unexplored. To address this issue, we focus on the visual grounding task and analyze the role and characteristics of entropy in comparison to reasoning tasks. Building on these findings, we introduce ECVGPO (Entropy Control Visual Grounding Policy Optimization), an interpretable algorithm designed for effective entropy regulation. Through entropy control, the trade-off between exploration and exploitation is better balanced. Experiments show that ECVGPO achieves broad improvements across various benchmarks and models. |
| title | The Role of Entropy in Visual Grounding: Analysis and Optimization |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2512.06726 |