# L2T-Tune:LLM-Guided Hybrid Database Tuning with LHS and TD3

arXiv: https://arxiv.org/abs/2511.01602 · HTML: https://mlsanity.com/paper/2511.01602/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [The Case for Learned Index Structures](https://mlsanity.com/paper/1712.01208/) (2017)
- [ALEX: An Updatable Adaptive Learned Index](https://mlsanity.com/paper/1905.08898/) (2019)
- [FITing-Tree: A Data-aware Index Structure](https://mlsanity.com/paper/1801.10207/) (2018)
- [BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning](https://mlsanity.com/paper/1710.03439/) (2017)
- [DB-BERT: a Database Tuning Tool that "Reads the Manual"](https://mlsanity.com/paper/2112.10925/) (2021)
- [λ-Tune: Harnessing Large Language Models for Automated Database System Tuning](https://mlsanity.com/paper/2411.03500/) (2024)
- [Distributed Learning Systems with First-order Methods](https://mlsanity.com/paper/2104.05245/) (2021)
- [Towards Dynamic and Safe Configuration Tuning for Cloud Databases](https://mlsanity.com/paper/2203.14473/) (2022)
- [E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model](https://mlsanity.com/paper/2404.11581/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Case for Agentic Tuning: From Documentation to Action in PostgreSQL](https://mlsanity.com/paper/2605.19988/) (2026)

## Abstract (arXiv)

Configuration tuning is critical for database performance. Although recent advancements in database tuning have shown promising results in throughput and latency improvement, challenges remain. First, the vast knob space makes direct optimization unstable and slow to converge. Second, reinforcement learning pipelines often lack effective warm-start guidance and require long offline training. Third, transferability is limited: when hardware or workloads change, existing models typically require substantial retraining to recover performance. To address these limitations, we propose L2T-Tune, a new LLM-guided hybrid database tuning framework that features a three-stage pipeline: Stage one performs a warm start that simultaneously generates uniform samples across the knob space and logs them into a shared pool; Stage two leverages a large language model to mine and prioritize tuning hints from manuals and community documents for rapid convergence. Stage three uses the warm-start sample pool to reduce the dimensionality of knobs and state features, then fine-tunes the configuration with the Twin Delayed Deep Deterministic Policy Gradient algorithm. We conduct experiments on L2T-Tune and the state-of-the-art models. Compared with the best-performing alternative, our approach improves performance by an average of 37.1% across all workloads, and by up to 73% on TPC-C. Compared with models trained with reinforcement learning, it achieves rapid convergence in the offline tuning stage on a single server. Moreover, during the online tuning stage, it only takes 30 steps to achieve best results.