arXiv Machine Learning August 6, 2026 By Marko Sterbentz, Kevin Cushing, Cameron Barrie, Kristian J. Hammond

RingSQL: Schema-Independent Synthetic Data Generation for Text-to-SQL Reinforcement Learning

Read the original on arXiv Machine Learning →

arXiv:2601. 05451v2 Announce Type: replace Abstract: Recent advances in text-to-SQL have been driven by larger models, better datasets, and new training methods like RLVR.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Computation and Language

Aug 24

Human-Level Text-to-SQL via Reinforcement Learning on Verified Data, Without Pipeline Engineering

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

llms reinforcement-learning fine-tuning benchmarks data-engineering

arXiv Computation and Language

Sep 14

Text-to-SPARQL Generation with Reinforcement Learning: A GRPO-based Approach on DBLP

arXiv:2605.20066v2 Announce Type: replace Abstract: Knowledge graph question answering seeks to translate natural language questions into executable queries over knowledge graphs, but existing approa...

By Jann Pfeifer, Debayan Banerjee, Ricardo Usbeck

llms nlp reinforcement-learning fine-tuning benchmarks

arXiv Computation and Language

Sep 21

COAL-SQL: Coverage-Guided Augmentation and Failure-Driven Learning for Text-to-SQL Post-Training

arXiv:2609.20842v1 Announce Type: new Abstract: Text-to-SQL translates natural-language questions into executable SQL queries, but open-source large language models still require task-specific post-t...

By Qifeng Cai, Xuanguang Pan, Hao Liang, Chang Xu, Wentao Zhang

llms reinforcement-learning fine-tuning data-engineering

arXiv Machine Learning

Jun 10

$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data

arXiv:2605. 01248v3 Announce Type: replace Abstract: Reinforcement learning (RL) post-training has enabled newer capabilities in models, such as agentic tool-use for search.

By Harsh Goel, Akhil Udathu, Susmija Jabbireddy, Pradnesh Kalkar, Atharva Parulekar

agents reinforcement-learning

arXiv AI

Jul 1

Test-Time Verification for Text-to-SQL via Outcome Reward Models

arXiv:2606. 30851v1 Announce Type: cross Abstract: Improving the reliability of large language models (LLMs) at inference time is a central challenge in structured reasoning tasks such as Text-to-SQL.

By Mattia Tritto, Giuseppe Farano, Dario Di Palma, Gaetano Rossiello, Fedelucio Narducci, Dharmashankar Subramanian, Tommaso Di Noia

llms benchmarks safety data-engineering

arXiv AI

Jul 17

QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL

arXiv:2406. 10593v3 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) for specific domain tasks has achieved great success in Text-to-SQL tasks.

By Yinggang Sun, Ziming Guo, Haining Yu, Chuanyi Liu, Xiang Li, Bingxuan Wang, Xiangzhan Yu, Tiancheng Zhao

llms fine-tuning data-engineering

One email a morning, machine-written

Related stories

Human-Level Text-to-SQL via Reinforcement Learning on Verified Data, Without Pipeline Engineering

Text-to-SPARQL Generation with Reinforcement Learning: A GRPO-based Approach on DBLP

COAL-SQL: Coverage-Guided Augmentation and Failure-Driven Learning for Text-to-SQL Post-Training

$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data

Test-Time Verification for Text-to-SQL via Outcome Reward Models

QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL