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 for instructions.

DeepRetrieval Paper

@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}
  }