SQL-R1: Training Natural Language to SQL Reasoning Model By Reinforcement Learning

Peixian Ma1,2, Xialie Zhuang1,3, Chengjin Xu1,4, Xuhui Jiang1,4, Ran Chen1, Jian Guo1
1IDEA Research, International Digital Economy Academy
2The Hong Kong University of Science and Technology (Guangzhou)
3University of Chinese Academy of Science
4DataArc Tech Ltd.
Paper Code arXiv

Introduction

Natural Language to SQL (NL2SQL) enables intuitive interactions with databases by transforming natural language queries into structured SQL statements. Despite recent advancements in enhancing human-computer interaction within database applications, significant challenges persist, particularly regarding the inference performance in complex scenarios involving multi-table joins and nested queries.

Current methodologies primarily utilize supervised fine-tuning (SFT) to train the NL2SQL model, which may limit adaptability and interpretability in new environments (e.g., finance and healthcare). In order to enhance the reasoning performance of the NL2SQL model in the above complex situations, we introduce SQL-R1, a novel NL2SQL reasoning model trained by the reinforcement learning (RL) algorithms.

SQL-R1 Overview

Experiments

Experiment Results

BibTeX

@article{ma2025sql,
  title={SQL-R1: Training Natural Language to SQL Reasoning Model By Reinforcement Learning},
  author={Ma, Peixian and Zhuang, Xialie and Xu, Chengjin and Jiang, Xuhui and Chen, Ran and Guo, Jian},
  journal={arXiv preprint arXiv:2504.08600},
  year={2025}
}