The Role of Entropy in Visual Grounding: Analysis and Optimization
This work addresses the problem of optimizing entropy control in visual grounding for researchers and practitioners in multimodal AI, representing an incremental advancement in fine-tuning techniques.
The paper tackled the unexplored role of entropy in visual grounding tasks by analyzing its characteristics and introducing ECVGPO, an interpretable algorithm for entropy control, which achieved broad improvements across various benchmarks and models.
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.