CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation
Kushal Gajjar, Harshit Sikchi, Arpit Singh Gautam, Marc Hammons, Saurabh Jha

TL;DR
CogniSQL-R1-Zero introduces a lightweight reinforcement learning framework that significantly improves SQL generation accuracy from natural language, achieving state-of-the-art results with less computational resources.
Contribution
The paper presents a novel RL-based approach for Text-to-SQL translation that avoids complex supervision and reward shaping, demonstrating superior performance with a smaller model and training setup.
Findings
Achieves state-of-the-art execution accuracy on Text2SQL benchmark.
Outperforms larger supervised models despite smaller size.
Efficient training on limited hardware resources.
Abstract
Translating natural language into SQL (Text-to-SQL) remains a core challenge at the intersection of language understanding and structured data access. Although large language models (LLMs) have improved fluency, generating correct and executable SQL, especially for complex queries, continues to be challenging. We introduce CogniSQL-R1-Zero, a reinforcement learning (RL) framework and model that produces accurate SQL using a lightweight reward signal based on execution correctness and format-tag compliance. By avoiding intermediate supervision, hybrid pipelines and complex reward shaping, our method encourages stable learning and stronger alignment with the ultimate task objective-producing executable programs. CogniSQL-R1-Zero achieves state-of-the-art execution accuracy on Text2SQL benchmark; BIRD bench, outperforming prior supervised and instruction-tuned baselines including SFT…
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Big Data and Digital Economy
MethodsShrink and Fine-Tune
