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: 13051
Venue: VLDB
Year: 2023
Pagerank: 4.3950066e-05
Overall Rank: 9,108 | 36.64%
DOI: 10.14778/3594512.3594525

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,885	PilotScope: Steering Databases with Machine Learning Drivers	2024	VLDB	4.895386e-05
9,960	An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL	2025	SIGMOD	4.2294678e-05
10,385	Optimizing Block Skipping for High-Dimensional Data with Learned Adaptive Curve	2025	SIGMOD	4.1945683e-05
10,630	Conformal Prediction for Verifiable Learned Query Optimization	2025	VLDB	4.1945683e-05
10,859	Graph Transformers for Query Plan Representation: Potentials and Challenges	2025	VLDB	4.1945683e-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
71	How Good Are Query Optimizers, Really?	2016	VLDB	0.00059038975
333	Neo: A Learned Query Optimizer	2019	VLDB	0.00027206884
640	Bao: Making Learned Query Optimization Practical	2021	SIGMOD	0.00018759152
2,121	Balsa: Learning a Query Optimizer Without Expert Demonstrations	2022	SIGMOD	9.5017232e-05
3,142	Active Learning for ML Enhanced Database Systems	2020	SIGMOD	7.4815444e-05
4,690	Deploying a Steered Query Optimizer in Production at Microsoft	2022	SIGMOD	5.997226e-05

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