This repository implements an RL-based approach for training efficient search agents in RAG (Retrieval Augmented Generation) systems. The method uses verifier-based reinforcement learning to optimize search agent behavior with minimal training data. Published at EMNLP 2025, it focuses on improving both the effectiveness and efficiency of retrieval-augmented language model applications.