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---
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license: mit
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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---
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# DeepRetrieval
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## Overview
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DeepRetrieval is a novel approach that uses reinforcement learning (RL) to train Large Language Models (LLMs) for query generation without requiring supervised data. Instead of relying on expensive human-annotated or distilled reference queries, DeepRetrieval enables LLMs to learn through direct trial and error, using retrieval metrics as rewards.
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## Key Features
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- **No Supervision Required**: Eliminates the need for expensive human-annotated or distilled reference queries
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- **RL-Based Framework**: Uses reinforcement learning to optimize query generation directly for retrieval performance
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- **State-of-the-Art Performance**: Achieves remarkable results across diverse retrieval tasks
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Please view our [GitHub page](https://github.com/pat-jj/DeepRetrieval) for instructions.
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[DeepRetrieval Paper](arxiv.org/abs/2503.00223)
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```
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@article{jiang2025deepretrievalhackingrealsearch,
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title={DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning},
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author={Pengcheng Jiang and Jiacheng Lin and Lang Cao and Runchu Tian and SeongKu Kang and Zifeng Wang and Jimeng Sun and Jiawei Han},
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year={2025},
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journal = {arXiv preprint arXiv: 2503.00223},
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url={https://arxiv.org/abs/2503.00223}
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}
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``` |
