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<title>SQL-R1: Training Natural Language to SQL Reasoning Model By Reinforcement Learning</title>
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<h1 class="title is-1 publication-title">SQL-R1: Training Natural Language to SQL Reasoning Model By Reinforcement Learning</h1>
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<div class="is-size-5 publication-authors">
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<span class="author-block">
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<a href="https://github.com/MPX0222" target="_blank">Peixian Ma</a><sup>1,2</sup>,</span>
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<span class="author-block">
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<a href="#" target="_blank">Xialie Zhuang</a><sup>1,3</sup>,</span>
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<a href="#" target="_blank">Chengjin Xu</a><sup>1,4</sup>,</span>
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<a href="#" target="_blank">Xuhui Jiang</a><sup>1,4</sup>,</span>
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<span class="author-block">
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<a href="#" target="_blank">Ran Chen</a><sup>1</sup>,</span>
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<span class="author-block">
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<a href="#" target="_blank">Jian Guo</a><sup>1</sup>
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<sup>1</sup>IDEA Research, International Digital Economy Academy<br>
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<sup>2</sup>The Hong Kong University of Science and Technology (Guangzhou)<br>
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<sup>3</sup>University of Chinese Academy of Science<br>
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<sup>4</sup>DataArc Tech Ltd.<br>
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</span>
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<h2 class="title is-3">Introduction</h2>
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<p>
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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.
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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.
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<img src="images/overview.png" alt="SQL-R1 Overview" style="width: 100%; height: auto;"/>
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<h2 class="title is-3">Experiments</h2>
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<img src="images/table1.png" alt="Experiment Results" style="width: 100%; height: auto;"/>
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<h2 class="title">BibTeX</h2>
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<pre><code>@article{ma2025sql,
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title={SQL-R1: Training Natural Language to SQL Reasoning Model By Reinforcement Learning},
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author={Ma, Peixian and Zhuang, Xialie and Xu, Chengjin and Jiang, Xuhui and Chen, Ran and Guo, Jian},
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journal={arXiv preprint arXiv:2504.08600},
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year={2025}
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}</code></pre>
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