@inproceedings{stoisser-etal-2025-sparks,
    title = "Sparks of Tabular Reasoning via {T}ext2{SQL} Reinforcement Learning",
    author = "Stoisser, Josefa Lia  and
      Martell, Marc Boubnovski  and
      Fauqueur, Julien",
    editor = "Chang, Shuaichen  and
      Hulsebos, Madelon  and
      Liu, Qian  and
      Chen, Wenhu  and
      Sun, Huan",
    booktitle = "Proceedings of the 4th Table Representation Learning Workshop",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.trl-workshop.20/",
    pages = "229--240",
    ISBN = "979-8-89176-268-8",
    abstract = "This work reframes the Text-to-SQL task as a pathway for teaching large language models (LLMs) to reason over and manipulate tabular data{---}moving beyond the traditional focus on query generation. We propose a two-stage framework that leverages SQL supervision to develop transferable table reasoning capabilities. First, we synthesize detailed chain-of-thought (CoT) traces from real-world SQL queries, providing step-by-step, clause-level supervision that teaches the model how to traverse, filter, and aggregate table fields. Second, we introduce a Group Relative Policy Optimization (GRPO) reinforcement learning objective that connects SQL execution accuracy to generalizable reasoning by encouraging steps that extend beyond task-specific syntax and transfer across datasets.Empirically, our approach improves performance on standard Text-to-SQL benchmarks and achieves substantial gains on reasoning-intensive datasets such as BIRD, CRT-QA and Tablebench, demonstrating enhanced generalization and interpretability. Specifically, the distilled-quantized LLaMA-8B model achieved a 34{\%} relative increase in exact match scores on CRT-QA when trained on Text-to-SQL tasks, while Qwen-2.5-7B achieved a 10{\%} and Qwen-2.5-14B a 6{\%} relative increase. These results suggest that SQL can serve not only as a target formalism but also as an effective scaffold for learning robust, transferable reasoning over structured data."
}