SERL‑SQL introduces a selective execution‑grounded reinforcement learning framework for multi‑turn Text‑to‑SQL agents. It samples on‑policy interaction trajectories and employs a training‑only teacher to re‑score student actions using execution feedback, converting the teacher‑student likelihood gap into bounded, masked weights that reweight GRPO advantages only on SQL and tool‑action tokens. Experiments on BIRD, Spider, and cross‑domain benchmarks show competitive performance, achieving 76.56% execution accuracy on BIRD‑Dev and 89.92% on Spider‑Test, and the reward‑based selection strategy approaches the oracle Best‑of‑N upper bound.
By Tao Liu, Tao Feng, Xiangheng Li, Jinwang Song, Yifan Li, Xiaoqing Cheng, Dixuan Zhang, Siquan Li, Lin Lan, Hongying Zan, Kunli Zhang, Chao Wu
arXiv:2603.20004v4 Announce Type: replace-cross
Abstract: Translating natural language questions to SQL queries (Text-to-SQL) is a long-standing problem in database research. Recent efforts have focu...
By Yuxuan Zhu, Tengjun Jin, Yoojin Choi, Daniel Kang
arXiv:2607. 20489v1 Announce Type: new Abstract: Text-to-SQL has advanced rapidly with large language models, but complex database queries still require reasoning beyond one-shot generation, including multi-step decomposition, execution-based diagnosis, and targeted correction.
By Jiawei Zhou, Jianwei Wang, Chenyu Zhou, Chaojian Shi, Ming Dong, Kai Wang
arXiv:2606. 01246v1 Announce Type: new Abstract: Text-to-SQL on complex schemas is unreliable on a single pass, so recent systems generate multiple SQL candidates and let voting filter out errors.
By Leo Luo, Haining Xie, Siqi Shen, Zhipeng Ma, Rui Ling, Hang Xu, Hefeng Jiang, Dingwei Chen, Yang Li, Peng Chen, Jie Jiang
J-Zero introduces a unified Challenger–Solver–Judge self‑evolution framework that operates without any initial data. The Challenger and Solver co‑evolve through adversarial task generation and response improvement, while the Judge adapts using known preference pairs derived from the Solver’s outputs rather than its own scores. Experiments show J‑Zero surpasses baselines by 4.2 points on verifiable tasks and 8.0 points on unverifiable tasks, maintaining improvement over ten iterations versus baseline degradation after two.
By Gyouk Chu, Myeongho Jeon, Teresa Yeo, Eunho Yang
arXiv:2601. 07055v2 Announce Type: replace Abstract: As high-quality data becomes increasingly difficult to obtain, self-evolution without curated training data has emerged as a promising paradigm.
By Zhenrui Yue, Kartikeya Upasani, Xianjun Yang, Suyu Ge, Shaoliang Nie, Yuning Mao, Zhe Liu, Dong Wang