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Iterated Population Based Training with Task-Agnostic Restarts

arXiv — cs.LG•Thursday, November 13, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The recent introduction of Iterated Population Based Training (IPBT) marks a significant advancement in hyperparameter optimization (HPO) for neural networks. This novel approach dynamically adjusts hyperparameters through task-agnostic restarts, utilizing time-varying Bayesian optimization to reinitialize settings. Evaluations across eight image classification and reinforcement learning tasks demonstrate that IPBT matches or outperforms five previous PBT variants and other HPO methods, such as random search and ASHA, without necessitating additional resources or changes to hyperparameters. The importance of hyperparameter updates is underscored, as the frequency of these updates can greatly influence performance. The source code for IPBT is publicly available, promoting further research and application in the field of artificial intelligence.
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