SEARCH-R1: LLMs Learn to Reason and Search via Reinforcement Learning

This research paper introduces SEARCH-R1, a novel framework that enhances large language models by enabling them to learn to effectively use search engines through reinforcement learning. This approach allows LLMs to autonomously generate search queries and leverage retrieved information during their reasoning process, improving performance on question-answering tasks. Unlike traditional methods, SEARCH-R1 optimizes the interaction with search in an end-to-end manner, using techniques like retrieved token masking for stable training and a simple reward system based on the accuracy of the final answer. Experiments demonstrate significant performance gains over strong baselines across various datasets, highlighting the potential of reinforcement learning for developing search-augmented reasoning in LLMs.