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v1.2.2 update todo-list, resources
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README.md

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## 🚀 Coming Soon Checklist
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- [ ] 📝 Update the camera-ready version of the paper, homepage and poster. coming sooooon!
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- [x] 📊 Release model weights on HuggingFace and ModelScope
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- [x] 🔧 Open source training code and RL dataset
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- [x] 📝 Detailed documentation

index.html

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<script src="static/js/bulma-carousel.min.js"></script>
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<script src="static/js/bulma-slider.min.js"></script>
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<script src="static/js/index.js"></script>
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<script>
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window.MathJax = {
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tex: {
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}
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};
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</script>
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</head>
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<img src="static/images/NeurIPS-logo.svg" alt="NeurIPS 2025" style="height: 60px;"/>
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<img src="static/images/idea_logo.png" alt="IDEA Research" style="height: 80px;"/>
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<img src="static/images/hkust_logo.png" alt="HKUST(GZ)" style="height: 90px;"/>
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<div class="institution-logo">
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<img src="static/images/ucas_logo.png" alt="UCAS" style="height: 55px; padding-left: 10px;"/>
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</section>
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<div class="hero-body">
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<div class="is-size-5 publication-authors">
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<span class="author-block">
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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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<sup>1</sup>IDEA Research, International Digital Economy Academy,
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<sup>2</sup>The Hong Kong University of Science and Technology (Guangzhou),
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<sup>3</sup>University of Chinese Academy of Science,
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<sup>4</sup>DataArc Tech Ltd.
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</span>
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<!-- <span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span> -->
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<span>arXiv</span>
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</a>
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</span>
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<span class="link-block">
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<a href="https://huggingface.co/MPX0222forHF/SQL-R1-7B" target="_blank"
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class="external-link button is-normal is-rounded is-dark">
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<span class="icon">
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🤗
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<span>HuggingFace</span>
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</a>
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</span>
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<h2 class="title is-3">Introduction</h2>
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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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Natural Language to SQL (NL2SQL, or Text2SQL) converts natural language questions~(NL) into structured SQL statements, simplifying database interaction without requiring database expertise. Recent advancements in NL2SQL have significantly enhanced the level of human-computer interaction within database query applications and contribute to a wide range of data science analysis tasks. Current NL2SQL models mainly focus on optimizing workflows and their components, such as schema linking, content retrieval, generation correction.
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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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<p><strong>Example formula:</strong></p>
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<p>$$\mathcal{L}_{RL} = \mathbb{E}_{(s,a) \sim \pi_\theta}[r(s,a) \cdot \log \pi_\theta(a|s)]$$</p>
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<h3 class="title is-4">Reinforcement Learning Framework</h3>
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<div class="method-subsection" style="margin-top: 2rem;">
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<h3 class="title is-4">Reward Function Design</h3>
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<h2 class="title is-3">Experiments</h2>
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<p class="has-text-grey" style="margin-top: 0.5rem; font-size: 0.9rem;">
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<strong>Table 1:</strong> Performance comparison on benchmark datasets
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<p style="color: #888; font-style: italic;">Ablation Figure 1</p>
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<p style="color: #888; font-size: 0.9rem;">Component analysis results</p>
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<p style="color: #888; font-style: italic;">Ablation Figure 2</p>
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<p style="color: #888; font-size: 0.9rem;">Performance breakdown</p>
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static/css/index.css

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