# SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer

arXiv: https://arxiv.org/abs/2508.17556 · HTML: https://mlsanity.com/paper/2508.17556/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2025.

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications](https://mlsanity.com/paper/2510.16724/) (2025)
- [Hard Constraints Meet Soft Generation: Guaranteed Feasibility for LLM-based Combinatorial Optimization](https://mlsanity.com/paper/2602.01090/) (2026)
- [GenDB: The Next Generation of Query Processing -- Synthesized, Not Engineered](https://mlsanity.com/paper/2603.02081/) (2026)
- [Piece of CAKE: Adaptive Execution Engines via Microsecond-Scale Learning](https://mlsanity.com/paper/2602.04181/) (2026)

## Abstract (arXiv)

Query optimization is a crucial problem in database systems that has been studied for decades. Learned query optimizers (LQOs) can improve performance over time by incorporating feedback; however, they suffer from cold-start issues and often require retraining when workloads shift or schemas change. Recent LLM-based query optimizers leverage pre-trained and fine-tuned LLMs to mitigate these challenges. Nevertheless, they neglect LLMs' in-context learning and execution records as feedback for continuous evolution. In this paper, we present SEFRQO, a Self-Evolving Fine-tuned RAG-based Query Optimizer. SEFRQO mitigates the cold-start problem of LQOs by continuously learning from execution feedback via a Retrieval-Augmented Generation (RAG) framework. We employ both supervised fine-tuning and reinforcement fine-tuning to prepare the LLM to produce syntactically correct and performance-efficient query hints. Moreover, SEFRQO leverages the LLM's in-context learning capabilities by dynamically constructing prompts with references to similar queries and the historical execution record of the same query. This self-evolving paradigm iteratively optimizes the prompt to minimize query execution latency. Evaluations show that SEFRQO outperforms state-of-the-art LQOs, achieving up to 65.05% and 93.57% reductions in query latency on the CEB and Stack workloads, respectively, compared to PostgreSQL.