HES-SQL: Hybrid Reasoning for Efficient Text-to-SQL with Structural Skeleton Guidance

• URL: http://arxiv.org/abs/2510.08896v1
• Date: Fri, 10 Oct 2025 01:15:57 GMT
• Title: HES-SQL: Hybrid Reasoning for Efficient Text-to-SQL with Structural Skeleton Guidance
• Authors: Suming Qiu, Jing Li, Zhicheng Zhou, Junjie Huang, Linyuan Qiu, Zhijie Sun,
• Abstract summary: We present HES-, a novel hybrid training framework that advances Text-to-latency generation through the integration of thinking-mode-fused supervised fine-tuning.<n>This framework enables switch between reasoning and non-reasoning modes while improving query accuracy and execution efficiency.
• Score: 6.653834890554154
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present HES-SQL, a novel hybrid training framework that advances Text-to-SQL generation through the integration of thinking-mode-fused supervised fine-tuning (SFT) with Group Relative Policy Optimization (GRPO). Our approach introduces three key innovations: (1) a skeleton-completeness scoring mechanism that enhances preference alignment between generated queries and optimal SQL structures; (2) a query-latency-aware reward system that incentivizes the generation of computationally efficient SQL queries; (3) a self-distillation process for thinking-mode completion that prevents degradation of the model's reasoning capabilities. This framework enables hybrid thinking models to switch between reasoning and non-reasoning modes while improving SQL query accuracy and execution efficiency. Experimental evaluation, conducted on MySQL 8.0 and SQLite 3.42 under controlled single-user conditions, demonstrates that HES-SQL achieves competitive performance with execution accuracies of 79.14\% and 54.9\% on the BIRD and KaggleDBQA benchmarks, respectively. Query latency is measured as the end-to-end execution time of generated queries on the DBMS, averaged over multiple runs to mitigate variance. Efficiency gains range from 11\% to 20\% relative to supervised baselines. Our results establish a new paradigm for Text-to-SQL systems that effectively balances semantic accuracy with computational efficiency through execution-informed reinforcement learning (RL). The proposed methodology has significant implications for developing robust natural language interfaces to databases and can be extended to broader structured generation tasks requiring both correctness and efficiency optimization.

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