Reinforcement Learning-Based Multi-Teacher Knowledge Distillation for Enhancing Retrieval Ranking Consistency

Abstract

Knowledge Distillation, an effective model compression technique, transfers knowledge from a large teacher model to a smaller student model, reducing computational costs while maintaining model performance. In large-scale retrieval tasks, maintaining the consistency of retrieval result rankings is crucial. However, traditional distillation methods focus on aligning the feature vectors extracted by the student model with those of the teacher model, which often fails to preserve ranking consistency in complex retrieval tasks. To address this issue, we propose a reinforcement learning-based multi-teacher knowledge distillation framework to optimize ranking consistency. By incorporating reinforcement learning strategies, the framework dynamically selects and adjusts the weights of multiple teacher models, enabling the student model to better learn from different teachers and accurately maintain retrieval rankings. Experimental results demonstrate that the proposed method significantly improves ranking consistency and retrieval performance on several benchmark datasets.