PaVeRL-SQL: Text-to-SQL via Partial-Match Rewards and Verbal Reinforcement Learning

• URL: http://arxiv.org/abs/2509.07159v1
• Date: Mon, 08 Sep 2025 19:15:38 GMT
• Title: PaVeRL-SQL: Text-to-SQL via Partial-Match Rewards and Verbal Reinforcement Learning
• Authors: Heng Hao, Wenjun Hu, Oxana Verkholyak, Davoud Ataee Tarzanagh, Baruch Gutow, Sima Didari, Masoud Faraki, Hankyu Moon, Seungjai Min,
• Abstract summary: We present emphPaVeRL--------, a framework that combines emphPartial-Match Rewards and emphVerbal Reinforcement Learning to drive self-evaluation in reasoning language models (RLMs) for Text-to-context.<n> pipelines achieve state-of-the-art (SOTA) results on popular Text-to- benchmarks -- Spider, Spider 2.0, and BIRD.
• Score: 10.353862232815844
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Text-to-SQL models allow users to interact with a database more easily by generating executable SQL statements from natural-language questions. Despite recent successes on simpler databases and questions, current Text-to-SQL methods still suffer from low execution accuracy on industry-scale databases and complex questions involving domain-specific business logic. We present \emph{PaVeRL-SQL}, a framework that combines \emph{Partial-Match Rewards} and \emph{Verbal Reinforcement Learning} to drive self-improvement in reasoning language models (RLMs) for Text-to-SQL. To handle practical use cases, we adopt two pipelines: (1) a newly designed in-context learning framework with group self-evaluation (verbal-RL), using capable open- and closed-source large language models (LLMs) as backbones; and (2) a chain-of-thought (CoT) RL pipeline with a small backbone model (OmniSQL-7B) trained with a specially designed reward function and two-stage RL. These pipelines achieve state-of-the-art (SOTA) results on popular Text-to-SQL benchmarks -- Spider, Spider 2.0, and BIRD. For the industrial-level Spider2.0-SQLite benchmark, the verbal-RL pipeline achieves an execution accuracy 7.4\% higher than SOTA, and the CoT pipeline is 1.4\% higher. RL training with mixed SQL dialects yields strong, threefold gains, particularly for dialects with limited training data. Overall, \emph{PaVeRL-SQL} delivers reliable, SOTA Text-to-SQL under realistic industrial constraints. The code is available at https://github.com/PaVeRL-SQL/PaVeRL-SQL.

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