Reward-SQL: Boosting Text-to-SQL via Stepwise Execution-Aware Reasoning and Process-Supervised Rewards
arXiv:2505.04671v3 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) trained with reinforcement learning (RL) have improved Text-to-SQL performance. However, RL-based approaches still struggle with complex queries due to two key limitations: insufficient stepwise execution-aware reasoning grounded in database feedback, and the lack of process-level rewards for guiding reasoning optimization. To address these issues, we propose CoCTE, a divide-and-conquer and execution-aware reasoning framework that progressively composes SQL queries through intermediate vie
The continuous advancements in AI, particularly LLMs and RL, are pushing the boundaries of what these models can achieve in complex data interaction tasks like Text-to-SQL.
Improving the ability of LLMs to generate accurate and complex SQL queries via more sophisticated reasoning directly enhances their utility for data analysis, automation, and decision-making across various industries.
The proposed 'Reward-SQL' and 'CoCTE' framework introduces a more robust method for LLMs to generate complex SQL queries by incorporating stepwise execution-aware reasoning and process-level rewards, addressing previous limitations in handling intricate database interactions.
- · AI developers
- · Data scientists
- · Database administrators
- · SaaS companies leveraging LLMs for data interaction
- · Companies with less sophisticated Text-to-SQL solutions
- · Manual SQL query writers for complex tasks (long-term)
Enhanced natural language interfaces for databases become more reliable and powerful.
Increased automation of data extraction and analysis, reducing the need for specialized SQL knowledge in routine tasks.
Accelerated data-driven decision-making and potentially new forms of data exploration driven by highly capable AI agents.
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Read at arXiv cs.LG