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| # 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 | |
| - **Reasoning-Enhanced Generation**: Incorporates a structured generation method with explicit reasoning before query formulation | |
| - **State-of-the-Art Performance**: Achieves remarkable results across diverse retrieval tasks | |
| - **Parameter Efficiency**: With just 3B parameters, outperforms much larger models like GPT-4o and Claude-3.5-Sonnet | |
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| ## About | |
| DeepRetrieval was developed by researchers from the UIUC CS. For more information, visit the [GitHub repository](https://github.com/pat-jj/DeepRetrieval). |