| # CogniSQL: Lightweight Reinforced Reasoning for Efficient SQL Generation |
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| ## Overview |
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| Welcome to CogniSQL! This organization hosts research datasets and resources for advancing Text-to-SQL generation through reinforcement learning. Our work focuses on building efficient, execution-aligned SQL generation systems that scale effectively while maintaining accuracy on complex database queries. |
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| ## Research Focus |
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| CogniSQL develops novel approaches to translate natural language into SQL (Text-to-SQL) using: |
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| - **Reinforcement Learning (RL) Frameworks**: Lightweight reward signals based on execution correctness and format-tag compliance |
| - **Efficient Training**: State-of-the-art performance on a smaller 7B parameter backbone (compared to 236B+ models) |
| - **Execution-Aligned Generation**: Direct optimization for producing correct, executable SQL without intermediate supervision |
| - **Interpretable Reasoning**: Multi-path reasoning traces for better understanding of model behavior |
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| ## Key Achievements |
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| - **State-of-the-Art Results**: Outperforms SFT CodeS-7B, DeepSeek-Coder 236B, and Mistral 123B on BIRD benchmark |
| - **Efficient Training**: Trained on just 4 NVIDIA A100 GPUs (40GB VRAM each) |
| - **Resource-Constrained Deployment**: Enables practical Text-to-SQL systems for real-world applications |
| - **Open Research**: Two curated datasets released for community research |
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| ## Datasets |
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| This organization maintains two high-quality datasets: |
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| 1. **Reasoning_Traces**: 5,024 reasoning traces with varying context lengths for interpretable SQL generation |
| 2. **Positive_Sample_Corpus**: 36,356 weakly supervised queries, each annotated with six semantically diverse reasoning paths |
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| Both datasets are designed to support research in efficient and interpretable Text-to-SQL modeling. |
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| ## Citation |
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| If you use our datasets or research, please cite the following paper: |
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| ```bibtex |
| @article{gajjar2025cognisql, |
| title={CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation}, |
| author={Gajjar, Kushal and Sikchi, Harshit and Gautam, Arpit Singh and Hammons, Marc and Jha, Saurabh}, |
| journal={arXiv preprint arXiv:2507.06013}, |
| year={2025}, |
| url={https://arxiv.org/abs/2507.06013} |
| } |
| ``` |
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| **arXiv**: [2507.06013](https://arxiv.org/abs/2507.06013) |
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| ## Research Team |
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| - **Kushal Gajjar** |
| - **Harshit Sikchi** |
| - **Arpit Singh Gautam** |
| - **Marc Hammons** |
| - **Saurabh Jha** |
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| ## Applications |
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| Our work enables: |
| - Database query systems that understand natural language |
| - Efficient SQL generation in resource-constrained environments |
| - Interpretable AI systems with transparent reasoning traces |
| - Production-grade Text-to-SQL pipelines |
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| ## License |
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| Please refer to individual dataset cards for specific licensing information. |
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| ## Related Links |
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| - [Paper on arXiv](https://arxiv.org/abs/2507.06013) |
| - [Reasoning Traces Dataset](https://huggingface.co/datasets/CogniSQL/Reasoning_Traces) |
| - [Positive Sample Corpus Dataset](https://huggingface.co/datasets/CogniSQL/Positive_Sample_Corpus) |
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