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README.md

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+---
+license: mit
+---
+# DeepRetrieval
+## Overview
+
+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.
+## Key Features
+
+- **No Supervision Required**: Eliminates the need for expensive human-annotated or distilled reference queries
+- **RL-Based Framework**: Uses reinforcement learning to optimize query generation directly for retrieval performance
+- **State-of-the-Art Performance**: Achieves remarkable results across diverse retrieval tasks
+
+Please view our [GitHub page](https://github.com/pat-jj/DeepRetrieval) for instructions.
+
+[DeepRetrieval Paper](arxiv.org/abs/2503.00223)
+```
+@article{jiang2025deepretrievalhackingrealsearch,
+      title={DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning}, 
+      author={Pengcheng Jiang and Jiacheng Lin and Lang Cao and Runchu Tian and SeongKu Kang and Zifeng Wang and Jimeng Sun and Jiawei Han},
+      year={2025},
+      journal = {arXiv preprint arXiv: 2503.00223},
+      url={https://arxiv.org/abs/2503.00223}
+  }
+```