Optimizing Reasoning for Text-to-SQL with Execution Feedback

Text-to-SQL demands precise reasoning to convert natural language questions into structured queries.While large language models (LLMs) excel in many reasoning tasks, their ability to leverage Chain-of-Thought (CoT) reasoning for text-to-SQL remains underexplored.We identify critical limitations: zero-shot CoT offers minimal gains, and Direct Preference Optimization (DPO) applied without CoT yields marginal improvements.We propose ExCoT-DPO, a novel framework that iteratively optimizes open-source LLMs by combining CoT reasoning with off-policy and on-policy DPO, relying solely on execution accuracy as feedback.This approach eliminates the need for reward models or human-annotated preferences.Our experimental results demonstrate significant performance gains: ExCoT-DPO improves execution accuracy on BIRD from 57.37% to 68.51% and on Spider from 78.81% to 86.59% for LLaMA-3 70B, with Qwen-2.5-Coderdemonstrating similar improvements.Our best model achieves state-of-the-art performance in the single-model setting on both BIRD and Spider datasets, notably achieving 68.53% on the BIRD test set. 1