Learning When to Reason for Text-to-SQL via SFT and DPO

• URL: http://arxiv.org/abs/2607.22622v1
• Date: Wed, 17 Jun 2026 06:31:28 GMT
• Title: Learning When to Reason for Text-to-SQL via SFT and DPO
• Abstract summary: Recent Text-to-Thought methods rely heavily on reasoning-centric paradigms such as Chain-of-Thought (CoT)<n>We propose AutoThink, a framework that integrates an auto-thinking mechanism into both Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) on Text-to-Thought.<n>Our approach enables the model to dynamically bypass reasoning for simple queries while invoking deep CoT for complex queries.
• Score: 38.109957873399246
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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