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LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning
Summary: Constraint-aware SQL generation via RL: LearnedSQLGen steers query synthesis toward constraint satisfaction using exploration–exploitation and execution feedback. Finite-state machine enforces valid SQL; benchmarks show ~30% accuracy gain and 10–35× speedups.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h9f8096f073a1c663
Venue
SIGMOD
Year
2022
Pagerank
6.5553854e-05
Overall Rank
4,508 | 69.79%
DOI
10.1145/3514221.3526155
Incoming Non-self Citations Over Time
BibTeX Citation
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@inproceedings{zhang_sigmod22,
title = {{LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning}},
author = {Zhang, Lixi and Chai, Chengliang and Zhou, Xuanhe and Li, Guoliang},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3526155},
url = {https://dl.acm.org/doi/10.1145/3514221.3526155},
year = {2022}
}
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