Related papers: RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism

RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism

URL: http://arxiv.org/abs/2507.02962v4
Date: Fri, 01 Aug 2025 09:41:05 GMT
Title: RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism
Authors: Zhiwen Tan, Jiaming Huang, Qintong Wu, Hongxuan Zhang, Chenyi Zhuang, Jinjie Gu,
Abstract summary: Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks.<n>LLMs remain prone to generating hallucinated or outdated responses due to their static internal knowledge.<n>Recent advancements in Retrieval-Augmented Generation (RAG) methods have aimed to enhance models' search and reasoning capabilities.
Score: 10.288667305064065
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, while LLMs remain prone to generating hallucinated or outdated responses due to their static internal knowledge. Recent advancements in Retrieval-Augmented Generation (RAG) methods have aimed to enhance models' search and reasoning capabilities through reinforcement learning (RL). Although these methods demonstrate promising results, they face challenges in training stability and encounter issues such as substantial inference time and restricted capabilities due to reliance on single-query mode. In this paper, we propose RAG-R1, a novel training framework designed to enable LLMs to adaptively leverage internal and external knowledge during the reasoning process. We further expand the generation and retrieval processes within the framework from single-query mode to multi-query parallelism, with the aim of reducing inference time and enhancing the model's capabilities. Extensive experiments on seven question-answering benchmarks demonstrate that our method outperforms the strongest baseline by up to 13.2% and decreases inference time by 11.1%.

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