51 lines
2.1 KiB
Markdown
51 lines
2.1 KiB
Markdown
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---
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license: apache-2.0
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datasets:
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- hotpotqa/hotpot_qa
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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---
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## Model Card for RAG-R1
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### Model Details
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* **Model Name:** RAG-R1-mq-7b
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* **Version:** 1.0
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* **Model Type:** RAG
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* **Developers:** Zhiwen Tan, Jiaming Huang, Qintong Wu, Hongxuan Zhang, Chenyi Zhuang, Jinjie Gu
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[](https://arxiv.org/abs/2507.02962) [](https://github.com/inclusionAI/AWorld-RL/tree/main/RAG-R1)
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### Overview
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RAG-R1 is a deepsearch training framework designed to enable LLMs to adaptively leverage internal and external knowledge during the reasoning process.
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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.
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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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### Framework
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<img src="RAG-R1.png" style="width:100%;">
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<h5 align="center"> Overall framework of RAG-R1.</h5>
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### Performance
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<img src="RAG-R1-result.png" style="width:100%;">
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<h5 align="left">Performance comparisons on QA benchmarks under the EM metric. The best and second
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best results are bold and underlined, respectively.</h5>
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### Acknowledgements
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RAG-R1 is inspired by [Deepseek-R1](https://github.com/deepseek-ai/DeepSeek-R1) with its implementation based on [veRL](https://github.com/volcengine/verl) and [Search-r1](https://github.com/PeterGriffinJin/Search-R1). We deeply appreciate the contributions of these teams to open-source research and development.
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### Citation
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Please cite our repo if our works are helpful for your research.
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```
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@article{RAG-R1,
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title={RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism},
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author={Zhiwen Tan and Jiaming Huang and Qintong Wu and Hongxuan Zhang and Chenyi Zhuang and Jinjie Gu},
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journal={arXiv preprint arXiv:2507.02962},
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year={2025}
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}
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```
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