A Survey of Reinforcement Learning-Driven Knowledge Distillation: Techniques, Challenges, and Applications

As deep learning models are widely applied across various domains, a critical challenge is how to compress models while maintaining high reasoning capability. Knowledge distillation, an effective technique for model compression, has been used to enhance the performance of lightweight models. However, traditional distillation methods are limited when dealing with complex reasoning tasks. Reinforcement learning (RL) offers a novel approach to knowledge distillation by optimizing the reasoning strategies of teacher models, generating more efficient decision paths, and providing more valuable learning content for student models. This paper reviews the latest advancements in combining reinforcement learning with knowledge distillation, focusing on policy distillation, value function distillation, and dynamic reward-guided distillation methods. It also discusses the challenges faced by RL-driven distillation, such as simplifying complex strategies, addressing temporal dependencies, and balancing exploration and exploitation, and suggests possible solutions. Finally, this paper explores the applications of RL-driven knowledge distillation in fields such as game AI, robotic control, and dialogue systems, and outlines future research directions, including automated distillation, multimodal distillation, and challenges in federated learning.