yaoyueduzhen

Model Name: RAG-R1-mq-7b Version: 1.0 Model Type: RAG Developers: Zhiwen Tan, Jiaming Huang, Qintong Wu, Hongxuan Zhang, Chenyi Zhuang, Jinjie Gu [](https://arxiv.org/abs/2507.02962) [](https://github.com/inclusionAI/AWorld-RL/tree/main/RAG-R1) RAG-R1 is a deepsearch 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, aimed at 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%. Performance comparisons on QA benchmarks under the EM metric. The best and second best results are bold and underlined, respectively. Acknowledgements RAG-R1 is inspired by Deepseek-R1 with its implementation based on veRL and Search-r1. We deeply appreciate the contributions of these teams to open-source research and development. Citation Please cite our repo if our works are helpful for your research.

Model Name: RAG-R1-sq-7b Version: 1.0 Model Type: RAG Developers: Zhiwen Tan, Jiaming Huang, Qintong Wu, Hongxuan Zhang, Chenyi Zhuang, Jinjie Gu [](https://arxiv.org/abs/2507.02962) [](https://github.com/inclusionAI/AWorld-RL/tree/main/RAG-R1) RAG-R1 is a deepsearch 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, aimed at 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%. Performance comparisons on QA benchmarks under the EM metric. The best and second best results are bold and underlined, respectively. Acknowledgements RAG-R1 is inspired by Deepseek-R1 with its implementation based on veRL and Search-r1. We deeply appreciate the contributions of these teams to open-source research and development. Citation Please cite our repo if our works are helpful for your research.