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)

Paper ID: hddeec3dcd78afb60
Venue: SIGMOD
Year: 2026
Pagerank: 4.9793485e-05
Overall Rank: 10,596 | 28.76%
DOI: 10.1145/3769826

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Hanwen Liu (University of Southern California)
2. Qihan Zhang (University of Southern California)
3. Ryan Marcus (University of Pennsylvania)
4. Ibrahim Sabek (University of Southern California)

BibTeX Citation

@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}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,883	QDBO: A Real-time Quantum-augmented Database System Optimizer	2026	VLDB	4.9793485e-05
10,941	Ultron: History-Based Query Optimization at Databricks	2026	VLDB	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 22 of 22 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
15	How Good Are Query Optimizers, Really?	2016	VLDB	0.00061066921
145	Neo: A Learned Query Optimizer	2019	VLDB	0.0002908188
174	Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation	2024	VLDB	0.00026790979
194	Milvus: A Purpose-Built Vector Data Management System	2021	SIGMOD	0.00025636725
362	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00019989474
669	CAESURA: Language Models as Multi-Modal Query Planners	2024	CIDR	0.0001495987
1,199	Balsa: Learning a Query Optimizer Without Expert Demonstrations	2022	SIGMOD	0.00011563985
1,250	DB-BERT: A Database Tuning Tool that "Reads the Manual"	2022	SIGMOD	0.00011339256
1,734	Flow-Loss: Learning Cardinality Estimates That Matter	2021	VLDB	9.7545773e-05
2,210	Lero: A Learning-to-Rank Query Optimizer	2023	VLDB	8.8257742e-05
2,231	GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization	2024	VLDB	8.7982985e-05
2,690	Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection	2022	VLDB	8.1258173e-05
3,487	LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans	2023	VLDB	7.263041e-05
3,949	Deploying a Steered Query Optimizer in Production at Microsoft	2022	SIGMOD	6.9052796e-05
4,202	Kepler: Robust Learning for Faster Parametric Query Optimization	2023	SIGMOD	6.7374091e-05
4,258	LEON: A New Framework for ML-Aided Query Optimization	2023	VLDB	6.6994722e-05
4,385	R-Bot: An LLM-based Query Rewrite System	2025	VLDB	6.6235293e-05
5,241	FASTgres: Making Learned Query Optimizer Hinting Effective	2023	VLDB	6.2154384e-05
5,788	Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries	2023	SIGMOD	5.9947442e-05
6,071	Demonstrating GPT-DB: Generating Query-Specific and Customizable Code for SQL Processing with GPT-4	2023	VLDB	5.8962612e-05
6,586	Can Large Language Models Be Query Optimizer for Relational Databases?	2026	SIGMOD	5.7430662e-05
8,016	Generating Succinct Descriptions of Database Schemata for Cost-Efficient Prompting of Large Language Models	2024	VLDB	5.4065977e-05

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