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.
Small LLMs with expert blocks are good enough for hyperparameter tuning
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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An LLM-based bounded controller adapts ML training parameters from structured telemetry to correct overfitting and exploration issues, shown on TinyStories and robotic RL tasks.
citing papers explorer
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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.
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AI Training Manager: Bounded Closed-Loop Control of Adaptive Training Recipes
An LLM-based bounded controller adapts ML training parameters from structured telemetry to correct overfitting and exploration issues, shown on TinyStories and robotic RL tasks.