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+# CogniSQL: Lightweight Reinforced Reasoning for Efficient SQL Generation
+
+## Overview
+
+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.
+
+## Research Focus
+
+CogniSQL develops novel approaches to translate natural language into SQL (Text-to-SQL) using:
+
+- **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
+
+## Key Achievements
+
+- **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
+
+## Datasets
+
+This organization maintains two high-quality datasets:
+
+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
+
+Both datasets are designed to support research in efficient and interpretable Text-to-SQL modeling.
+
+## Citation
+
+If you use our datasets or research, please cite the following paper:
+
+```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}
+}
+```
+
+**arXiv**: [2507.06013](https://arxiv.org/abs/2507.06013)
+
+## Research Team
+
+- **Kushal Gajjar**
+- **Harshit Sikchi**
+- **Arpit Singh Gautam**
+- **Marc Hammons**
+- **Saurabh Jha**
+
+## Applications
+
+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
+
+## License
+
+Please refer to individual dataset cards for specific licensing information.
+
+## Related Links
+
+- [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)