EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL
arXiv:2606.23693v1 Announce Type: new Abstract: Text-to-SQL enables users to query databases using natural language by generating executable SQL queries. Recent methods have increasingly adopted Large Language Models based reinforcement learning (RL) to leverage execution feedback for training. However, existing RL methods assign uniform query-level rewards to all clauses in a SQL query, treating correct and incorrect clauses equally. This coarse-grained reward design leads to insufficient learning signals for correct SQL generation. To address this issue, we propose EXPO-SQL (EXecution-based clause-level Policy Optimization for Text-to-SQL) which provides fine-grained supervision through clause-level rewards. To assign clause-level rewards, our method identifies erroneous clauses by analyzing execution results, including error messages and clause-wise incremental execution. Experiments on widely-used Text-to-SQL benchmarks demonstrate that EXPO-SQL significantly outperforms existing supervised fine-tuning, prompting, and RL-based methods through fine-grained clause-level learning. Our code is available at https://github. com/jhn25/EXPO-SQL.
[2606.23693] EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL
[Submitted on 29 Apr 2026]
Title:EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL
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Abstract:Text-to-SQL enables users to query databases using natural language by generating executable SQL queries. Recent methods have increasingly adopted Large Language Models based reinforcement learning (RL) to leverage execution feedback for training. However, existing RL methods assign uniform query-level rewards to all clauses in a SQL query, treating correct and incorrect clauses equally. This coarse-grained reward design leads to insufficient learning signals for correct SQL generation. To address this issue, we propose EXPO-SQL (EXecution-based clause-level Policy Optimization for Text-to-SQL) which provides fine-grained supervision through clause-level rewards. To assign clause-level rewards, our method identifies erroneous clauses by analyzing execution results, including error messages and clause-wise incremental execution. Experiments on widely-used Text-to-SQL benchmarks demonstrate that EXPO-SQL significantly outperforms existing supervised fine-tuning, prompting, and RL-based methods through fine-grained clause-level learning. Our code is available at https://github. com/jhn25/EXPO-SQL.
Comments: 20 pages, 8 figures
Subjects:
Computation and Language (cs.CL); Information Retrieval (cs.IR)
Cite as: arXiv:2606.23693 [cs.CL]
(or arXiv:2606.23693v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.23693
arXiv-issued DOI via DataCite
Journal reference: ACL 2026 Findings
Submission history
From: Jaehoon Lee [view email] [v1] Wed, 29 Apr 2026 10:33:16 UTC (763 KB)
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