A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning Enhanced Approach
Summary: LITune enables end-to-end automatic tuning of Learned Index Structures using a DRL-based pipeline for stable, efficient optimization. The O2 online updater adapts to workload shifts, delivering up to 98% runtime reduction and 17x throughput gains. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Taiyi Wang (University of Cambridge)
- 2. Liang Liang (EPFL)
- 3. Guang Yang (Imperial College London)
- 4. Thomas Heinis (Imperial College London)
- 5. Eiko Yoneki (University of Cambridge)
BibTeX Citation
@inproceedings{wang_sigmod25,
title = {{A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning Enhanced Approach}},
author = {Wang, Taiyi and Liang, Liang and Yang, Guang and Heinis, Thomas and Yoneki, Eiko},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725257},
url = {https://dl.acm.org/doi/10.1145/3725257},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,047 | SQL-Factory: A Multi-Agent Framework for High-Quality and Large-Scale SQL Generation | 2026 | VLDB | 5.3251649e-05 |
| 10,267 | Mathematical Foundations of Poisoning Attacks on Linear Regression over Cumulative Distribution Functions | 2026 | SIGMOD | 5.093636e-05 |
| 10,277 | On Self-Designing Learned Indexes | 2026 | SIGMOD | 5.093636e-05 |
| 10,461 | HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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