The field of machine learning is moving towards more efficient and data-driven approaches, with a focus on selecting the most informative samples and constructing compact subsets for training. This direction is driven by the need to reduce computational inefficiencies and improve model performance. Researchers are exploring methods to characterize category difficulty, identify informative subsets of training data, and optimize coreset selection for various tasks, including transfer learning and image classification. Notable papers in this area include those that propose novel frameworks for coreset selection, such as Non-Uniform Class-Wise Coreset Selection, which achieves superior accuracy and computational efficiency. Other papers, like DataS^3, introduce benchmarks and algorithms for dataset subset selection for specialization, highlighting the importance of tailored dataset curation for deployment-specific tasks.