熵在视觉定位中的作用：分析与优化
The Role of Entropy in Visual Grounding: Analysis and Optimization
AI总结：
本文提出ECVGPO算法，通过熵控制优化视觉定位任务，提升探索与利用的平衡，实现多基准的性能提升。
摘要：
近期，通过强化学习微调多模态大语言模型（MLLMs）的进展取得了显著成果，特别是在引入各种熵控制技术后尤为明显。然而，熵在以感知为导向的任务如视觉定位中的作用和特性，以及有效控制熵的策略仍鲜有研究。为解决这一问题，我们专注于视觉定位任务，分析熵在与推理任务相比的作用和特性。基于这些发现，我们引入了ECVGPO（熵控制视觉定位策略优化），一种可解释的算法，用于有效调节熵。通过熵控制，探索与利用之间的权衡得到更好的平衡。实验表明，ECVGPO在各种基准和模型上均实现了广泛的改进。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
