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Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective

Summary: Analyzes ML-centric pitfalls in learned query optimizers—training data generation, arbitrary train/validation splits, and inconsistent benchmarking—and introduces a standardized end-to-end ML evaluation framework for LQOs. Rigorous cross-split experiments show PostgreSQL often outperforms current LQOs, challenging claimed RL-driven gains. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13398
Venue
VLDB
Year
2024
Pagerank
4.8643538e-05
Overall Rank
7,008 | 51.25%
DOI
10.14778/3654621.3654625

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