SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer
Summary: SEFRQO is a self-evolving RAG-based query optimizer that fine-tunes LLMs (supervised + reinforcement) to produce performance-oriented query hints by retrieving execution-feedback. It dynamically builds prompts from similar queries and per-query execution records for continual in-context learning, cutting latency up to ~65–94% vs prior LQOs.
(summarized by gpt-5-mini on Feb 11 2026)
@inproceedings{liu_sigmod26,
title = {{SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer}},
author = {Liu, Hanwen and Zhang, Qihan and Marcus, Ryan and Sabek, Ibrahim},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769826},
url = {https://dl.acm.org/doi/10.1145/3769826},
year = {2026}
}
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