arXiv:2606.06825v1 Announce Type: cross Abstract: Reinforcement learning has recently shown promise in improving large language models for Text-to-SQL generation, yet existing methods typically optimize one-shot rewards defined over a single SQL state. Such rewards provide limited guidance for iterative SQL correction and are insufficient to capture the improvement of multi-turn SQL refinement. In this paper, we propose Progress-SQL, a multi-turn reinforcement learning framework with progressive rewards for Text-to-SQL. Our approach introduces an Oracle-guided Diagnostic Tree (ODT), which abst
The continuous advancements in large language models necessitate more effective methods for fine-tuning and interaction, particularly in complex tasks like Text-to-SQL, pushing the innovation for improved reinforcement learning techniques.
This research outlines a method to significantly enhance the accuracy and multi-turn capabilities of AI models in translating natural language to structured queries, which is critical for broader enterprise data interaction and agentic systems.
Current reinforcement learning for Text-to-SQL primarily uses one-shot rewards; this introduces progressive rewards and an Oracle-guided Diagnostic Tree, enabling more effective iterative SQL correction and multi-turn refinement, directly impacting AI agent efficiency.
- · AI developers
- · Data analytics platforms
- · Enterprise AI users
- · Companies building AI agents
- · Traditional SQL query methods
- · AI models with poor multi-turn reasoning
- · Companies reliant on single-shot reward systems
Improved Text-to-SQL generation will lead to more reliable and autonomous data interactions through AI.
Enhanced data accessibility via natural language would accelerate business intelligence and decision-making processes across industries, reducing the need for specialized SQL knowledge.
This could contribute to the development of sophisticated autonomous AI agents capable of querying and manipulating complex databases without human oversight, fundamentally changing many white-collar workflows.
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Read at arXiv cs.AI