@inproceedings{dai-etal-2026-reex,
    title = "{R}e{E}x-{SQL}: Reasoning with Execution-Aware Reinforcement Learning for Text-to-{SQL}",
    author = "Dai, Yaxun  and
      Xie, Wenxuan  and
      Zhuang, Xialie  and
      Yang, Tianyu  and
      Liu, Ziyi  and
      Yang, Haiqin  and
      Yang, Yiying  and
      Zhao, Yuhang  and
      Chao, Pingfu  and
      Jiang, Wenhao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.35/",
    pages = "824--847",
    ISBN = "979-8-89176-390-6",
    abstract = "Current Text-to-SQL reasoning models often lack integrated execution feedback during generation, and most existing approaches utilize feedback only for post-hoc correction. This separation not only limits real-time error correction, but may also introduce mistakes by altering otherwise correct SQL queries. To address these challenges, we present **ReEx-SQL** (Reasoning with Execution-Aware Reinforcement Learning), a Text-to-SQL framework that interacts with the SQL execution engine during decoding and dynamically adjusts reasoning based on execution feedback, thereby enabling context-sensitive query refinement and improved accuracy. ReEx-SQL achieves this through structured prompts with markup tags and a stepwise rollout strategy that incorporates execution feedback at each generation stage. For policy supervision, we design a composite reward function{---}featuring an exploration reward{---}to explicitly encourage effective interaction with the database. Furthermore, ReEx-SQL adopts a tree-based decoding strategy to facilitate exploratory reasoning and primarily aims to enhance parallel decoding efficiency. Notably, ReEx-SQL achieves 89.1{\%} accuracy on Spider and 65.3{\%} on BIRD at the 7B scale, surpassing baseline models by 2.7{\%} and 2.6{\%}, respectively. In addition, its tree-based decoding accelerates inference by 51.9{\%} compared to linear decoding during sampling."
}