BASE: Bridging the Gap between Cost and Latency for Query Optimization

Summary: BASE: two-stage RL optimizer—train policy on cheap cost signals then transfer a calibrated reward function via an inverse-RL variant to align the policy to latency without costly online execution. Mutual reward–policy refinement improves latency over cost-only learners, cuts training time ≈30% vs SOTA, and generalizes to boost other learned optimizers. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: he45d1fa82df85eb0
Venue: VLDB
Year: 2023
Pagerank: 5.204289e-05
Overall Rank: 9,276 | 37.64%
DOI: 10.14778/3594512.3594525

Incoming Non-self Citations Over Time

Authors

1. Xu Chen (University of Electronic Science and Technology of China)
2. Zhen Wang (Alibaba)
3. Shuncheng Liu (University of Electronic Science and Technology of China)
4. Yaliang Li (Alibaba)
5. Kai Zeng (Alibaba)
6. Bolin Ding (Alibaba)
7. Jingren Zhou (Alibaba)
8. Han Su (University of Electronic Science and Technology of China)
9. Kai Zheng (University of Electronic Science and Technology of China)

BibTeX Citation

@article{chen_vldb23,
        title = {{BASE: Bridging the Gap between Cost and Latency for Query Optimization}},
        author = {Chen, Xu and Wang, Zhen and Liu, Shuncheng and Li, Yaliang and Zeng, Kai and Ding, Bolin and Zhou, Jingren and Su, Han and Zheng, Kai},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {8},
        pages = {1958--1966},
        doi = {10.14778/3594512.3594525},
        url = {https://doi.org/10.14778/3594512.3594525},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
5,871	PilotScope: Steering Databases with Machine Learning Drivers	2024	VLDB	5.9639223e-05
9,954	Conformal Prediction for Verifiable Learned Query Optimization	2025	VLDB	5.1038322e-05
9,956	Graph Transformers for Query Plan Representation: Potentials and Challenges	2025	VLDB	5.1038322e-05
10,336	An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL	2025	SIGMOD	5.0200193e-05
10,908	DBAgent: An RL-Based Agent for Autonomous Database Operations and Maintenance	2026	VLDB	4.9793485e-05
11,113	Optimizing Block Skipping for High-Dimensional Data with Learned Adaptive Curve	2025	SIGMOD	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 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
362	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00019989474
1,199	Balsa: Learning a Query Optimizer Without Expert Demonstrations	2022	SIGMOD	0.00011563985
2,275	Active Learning for ML Enhanced Database Systems	2020	SIGMOD	8.7090584e-05
3,949	Deploying a Steered Query Optimizer in Production at Microsoft	2022	SIGMOD	6.9052796e-05

Semantically Similar Papers

#	Overall Rank	Paper	Year	Venue
1	6,308	Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective	2024	VLDB
2	9,670	Low Rank Learning for Offline Query Optimization	2025	SIGMOD
3	6,710	Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis	2023	VLDB
4	4,711	Learned Index Benefits: Machine Learning Based Index Performance Estimation	2022	VLDB
5	461	An End-to-End Learning-based Cost Estimator	2020	VLDB
6	2,690	Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection	2022	VLDB
7	1,199	Balsa: Learning a Query Optimizer Without Expert Demonstrations	2022	SIGMOD
8	4,482	LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning	2022	SIGMOD
9	8,332	Learned Offline Query Planning via Bayesian Optimization	2025	SIGMOD
10	3,060	Towards a Hands-Free Query Optimizer through Deep Learning	2019	CIDR