@inproceedings{lee-etal-2026-expo,
title = "{EXPO}-{SQL}: Execution-based Clause-level Policy Optimization for Text-to-{SQL}",
author = "Lee, Jaehoon and
Na, CheolWon and
Bae, Suyoung and
Lee, Jin-Seop and
Lee, Jihyung and
Choi, YunSeok and
Lee, Jee-Hyong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1107/",
pages = "22000--22019",
ISBN = "979-8-89176-395-1",
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** (**EX**ecution-based clause-level **P**olicy **O**ptimization 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."
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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** (**EX**ecution-based clause-level **P**olicy **O**ptimization 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.</abstract>
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%0 Conference Proceedings
%T EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL
%A Lee, Jaehoon
%A Na, CheolWon
%A Bae, Suyoung
%A Lee, Jin-Seop
%A Lee, Jihyung
%A Choi, YunSeok
%A Lee, Jee-Hyong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F lee-etal-2026-expo
%X 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** (**EX**ecution-based clause-level **P**olicy **O**ptimization 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.
%U https://aclanthology.org/2026.findings-acl.1107/
%P 22000-22019
Markdown (Informal)
[EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL](https://aclanthology.org/2026.findings-acl.1107/) (Lee et al., Findings 2026)
ACL
- Jaehoon Lee, CheolWon Na, Suyoung Bae, Jin-Seop Lee, Jihyung Lee, YunSeok Choi, and Jee-Hyong Lee. 2026. EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL. In Findings of the Association for Computational Linguistics: ACL 2026, pages 22000–22019, San Diego, California, United States. Association for Computational Linguistics.