---
title: "ReSQL — paper by Park et al. (2026) — summary and ideas"
description: "Small and mid-sized LLMs often produce SQL that fails to execute, and they are poor at repairing their own mistakes. 39 references."
authors: ["Minjun Park", "Yongju Seong", "Myoseop Sim", "Kyungkoo Min", "Stanley Jungkyu Choi"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1677/"
doi: "10.18653/v1/2026.findings-acl.1677"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/park-2026-resql-self-improving-framework-reasoning/"
---
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# ReSQL: Self-Improving Framework for Reasoning-Aware Text-to-SQL Dataset Generation

**Authors:** [Minjun Park](https://md.research.elamur.ai/free/en/authors/minjun-park/), Yongju Seong, Myoseop Sim, Kyungkoo Min and [Stanley Jungkyu Choi](https://md.research.elamur.ai/free/en/authors/stanley-jungkyu-choi/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1677/) · [PDF](https://aclanthology.org/2026.findings-acl.1677.pdf) · [DOI 10.18653/v1/2026.findings-acl.1677](https://doi.org/10.18653/v1/2026.findings-acl.1677) · [OpenAlex](https://openalex.org/W7166814043)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Small and mid-sized LLMs often produce SQL that fails to execute, and they are poor at repairing their own mistakes. On BIRD, up to 70.85% of their queries error out. ReSQL addresses this with a closed-loop pipeline in which a model builds training data from its own failures. The model runs over the SPIDER and BIRD training sets and records queries that fail execution. Given the gold SQL and the error message, it then writes a structured analysis in three steps: what the faulty query does, where it breaks, and how to fix it. These traces are merged with the original data to fine-tune the same model, with no distillation from a larger teacher. At inference, the traces are embedded, and the three examples closest to the question and error type serve as few-shot hints for revision. With this setup, 7B–9B open models surpass GPT-4 on both benchmarks. The work suggests that smarter pipeline design, not only model scale, can make Text-to-SQL more reliable.

## Key points
- Error data came from Llama-3.2/3.1, Qwen-2.5, Gemma-2 and Mistral-v0.3 models (1–9B) run on 7,000 SPIDER and 8,556 BIRD training examples. The code and all generated reasoning datasets are publicly released.
- The GPT-4 reference scores were 79.35% EX on SPIDER and 46.35% on BIRD, and ReSQL-trained 7B–9B models beat both. For Llama 1B, the gain over plain SFT reaches up to 83%.
- An ablation with Llama-3.2 3B (full system: 69.25 / 44.04 EX on SPIDER / BIRD) shows each component's contribution:
  - removing error reasoning costs the most (−11.03 / −17.61 points);
  - removing RAG has a smaller effect (−0.84 / −1.21);
  - a single revision pass instead of iterative refinement costs −3.39 / −3.78.
- Retrieval mainly helps with rare error types, such as "ORDER BY before UNION" (−73.1%) and aggregate/GROUP BY errors (−100%). Common semantic and syntax errors barely change.
- The method still depends on gold SQL, so "self-improving" refers to autonomous data generation, not label-free learning. Behaviour under true out-of-distribution shift has not been tested.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Minjun Park, Yongju Seong, Myoseop Sim, Kyungkoo Min and Stanley Jungkyu Choi. 2026. ReSQL: Self-Improving Framework for Reasoning-Aware Text-to-SQL Dataset Generation. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{park2026resql,
  title = {{ReSQL: Self-Improving Framework for Reasoning-Aware Text-to-SQL Dataset Generation}},
  author = {Minjun Park, Yongju Seong, Myoseop Sim, Kyungkoo Min and Stanley Jungkyu Choi},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1677},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1677}
}
```

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## References

39 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*