Reward-SQL enhances LLM Text-to-SQL with execution-aware reasoning

Researchers have developed Reward-SQL, a novel framework designed to enhance the performance of large language models (LLMs) in Text-to-SQL tasks. This approach addresses limitations in current RL-based methods by incorporating stepwise execution-aware reasoning and process-level rewards. Reward-SQL utilizes a divide-and-conquer strategy with intermediate view validation and structured Common Table Expressions (CTEs) to improve accuracy and interpretability. The framework includes a process reward model (PRM) that provides fine-grained, execution-aware supervision, which is then integrated into both RL training and inference stages to stabilize optimization and improve trajectory exploration. AI

IMPACT This research could lead to more accurate and interpretable SQL query generation from natural language, benefiting data analysis and database interaction.

RANK_REASON This is a research paper detailing a new method for improving LLM performance on Text-to-SQL tasks. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• LLMs
• Reward-SQL
• Yuxin Zhang

• paper
• model release

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