A multi-scale, multi-fidelity Bayesian Optimization (BO) approach where {data mixtures, model scale, training steps} are adaptively selected, achieving >2.6x speedups compared to multi-fidelity BO and random search baselines.

A Max K-armed Bandit using assumptions derived from empirical data that handles short-tailed and skewed distributions to dynamically allocate resources to hyperparameter optimization runs.