[Submitted on 17 Jun 2026]
Title:Learning When to Reason for Text-to-SQL via SFT and DPO
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Abstract:Recent Text-to-SQL methods rely heavily on reasoning-centric paradigms such as Chain-of-Thought (CoT), achieving substantial gains on complex benchmarks at the cost of high inference-time overhead. However, a large fraction of real-world queries are simple lookups or aggregations that can be resolved without multi-step deduction, making forced reasoning wasteful. Thus, we propose AutoThinkSQL, a framework that integrates an auto-thinking mechanism into both Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) on Text-to-SQL. Our approach enables the model to dynamically bypass reasoning for simple queries while invoking deep CoT for complex queries. On Qwen3-Coder-30B-A3B, our method achieves consistent gains compared to the best counterpart baseline on both Spider and BIRD benchmarks while simultaneously reducing average output tokens by 24.6% and 18.3%, and average latency by 17.1% and 11.5% compared to CoT-only generation. Further analysis indicates that the model learns to align its reasoning decisions with query difficulty.
Comments: 8 pages, 5 figures. Model checkpoints are available at this https URL
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.22622 [cs.CL]
(or arXiv:2607.22622v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.22622
arXiv-issued DOI via DataCite
Submission history
From: Soohyuk Jang [view email] [v1] Wed, 17 Jun 2026 06:31:28 UTC (51 KB)
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