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| license: mit | |
| title: DeepRetrieval | |
| emoji: 🚀 | |
| colorTo: purple | |
| pinned: true | |
| short_description: Hacking Real Search Engines and Retrievers! | |
| sdk: static | |
| # 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](https://arxiv.org/pdf/2503.00223) |