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Progress-SQL addresses the limitations of one-shot reward optimization in Text-to-SQL by introducing a multi-turn reinforcement learning framework with progressive rewards. The method leverages an Oracle-guided Diagnostic Tree to provide dense, structural feedback that enhances iterative SQL refinement and robustness across standard and adversarial benchmarks. This approach offers a significant methodological advance for improving the reliability of LLMs in complex database query generation tasks.
Progress-SQL: Improving Reinforcement Learning for Text-to-SQL via Progressive Rewards
arXiv:2606.06825v1 Announce Type: new 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 abstracts SQL queries into clause-level structural profiles and produces diagnostic feedback for next-turn refinement. To provide dense and robust reward signals, we combine ODT-based structural alignment with lexical alignment and define a progressive reward that measures the improvement from the initial SQL to the final SQL. We further incorporate a progression latency reward that favors earlier correctness and an execution status reward that encourages recovery from the invalid SQL. Experiments on BIRD, Spider, and Spider robustness variants demonstrate that our method consistently improves Text-to-SQL performance across both primary and robustness evaluations.
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 abstracts SQL queries into clause-level structural profiles and produces diagnostic feedback for next-turn refinement. To provide dense and robust reward signals, we combine ODT-based structural alignment with lexical alignment and define a progressive reward that measures the improvement from the initial SQL to the final SQL. We further incorporate a progression latency reward that favors earlier correctness and an execution status reward that encourages recovery from the invalid SQL. Experiments on BIRD, Spider, and Spider robustness variants demonstrate that our method consistently improves Text-to-SQL performance across both primary and robustness evaluations.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2606.06825 [cs.CL]
(or arXiv:2606.06825v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.06825 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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