s3
s3 is a framework designed for efficient and effective training of search agents in Retrieval-Augmented Generation (RAG) systems. Presented at EMNLP 2025, it utilizes Reinforcement Learning with Verifiable Rewards (RLVR) to optimize the search component of language models without altering the generator itself. The system enables large language models to search more effectively by focusing specifically on search strategies, achieving strong performance on question-answering tasks with significantly less training data compared to prior methods. s3 is modular and compatible with any black-box large language model, making it adaptable for various deployment scenarios. Technical requirements include Python 3.9, PyTorch 2.4.0, vLLM, Ray, and Flash Attention 2, with retrieval and evaluation handled via Pyserini. The framework supports a structured workflow covering installation, environment preparation, agent training, search execution, and performance evaluation, offering a streamlined approach to enhancing RAG cap