Reinforcement Learning for Effective Few-Shot Ranking

Neural rankers have achieved strong retrieval effectiveness but require large amounts of labeled data, limiting their applicability in few-shot settings. In this paper, we address the sample inefficiency of neural ranking methods by introducing a Reinforcement Learning (RL)-based re-ranking model that achieves high effectiveness with minimal training data. Built on a Deep Q-learning Network (DQN) framework, our approach is designed for few-shot settings, maximizing sample efficiency to ensure robust generalization from limited interactions. Extensive experiments show that our model significantly outperforms data-intensive methods and existing few-shot baselines, demonstrating RL's potential to enhance IR capabilities in few-shot scenarios.