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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
+
+## Performance Highlights
+
+- **Literature Search**: Doubles the recall on PubMed (65.07% vs previous SOTA 24.68%) and ClinicalTrials.gov (63.18% vs previous SOTA 32.11%)
+- **Evidence-Seeking Retrieval**: Achieves performance equivalent to industry-leading LLMs on NQ and TriviaQA, and significantly outperforms them on SQuAD
+- **Classic IR**: Shows superior performance across diverse retrieval benchmarks
+- **SQL Database Search**: Excels in text-to-SQL generation for database search
+
+
+## About
+
+DeepRetrieval was developed by researchers from the University of Illinois Urbana-Champaign. For more information, visit the [GitHub repository](https://github.com/pat-jj/DeepRetrieval).