On eight PMLB tabular benchmarks, an LLM HPO advisor adds only +0.40 pp CV accuracy beyond a fixed default seed and is overtaken by seeded classical methods within 5-12 evaluations, with no held-out test gain.
Revisiting OPRO : The limitations of small-scale LLMs as optimizers
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When Is an LLM Worth It for Hyperparameter Optimization? A Budget-Matched Study on Tabular Data Finds the Warm-Start Is a Default Configuration, Not the Model
On eight PMLB tabular benchmarks, an LLM HPO advisor adds only +0.40 pp CV accuracy beyond a fixed default seed and is overtaken by seeded classical methods within 5-12 evaluations, with no held-out test gain.