Title: Data-Efficient Training by Evolved Sampling
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: dynamic data selection, data-efficiency, training acceleration, frequency analysis

Abstract: Data selection is designed to accelerate learning with preserved performance. To achieve this, a fundamental thought is to identify informative data samples with significant contributions to the training. In this work, we propose **Evolved Sampling** (**ES**), a simple yet effective framework for *dynamic* sampling along the training process. This method conducts *batch* level data selection based on the dynamics of losses and augmented *loss differences*, which enables flexible *frequency tuning*, and hence significantly reduces the back propagation time with maintained model performance. Due to its conciseness, ES is also readily extensible to incorporate *set* level data selection (to form ES with pruning, **ESWP**) for further accelerations. As a plug-and-play framework, ES(WP) consistently achieves lossless training accelerations across various pre-training and post-training tasks, saving up to nearly 45\% wall-clock time. Our results motivate further investigations on the data efficiency aspect of modern large-scale machine learning.