The ICCV 2025 Workshop on Curated Data for Efficient Learning (CDEL) seeks to advance the understanding and development of data-centric techniques that improve the efficiency of training large-scale machine learning models. As model sizes continue to grow and data requirements scale accordingly, this workshop brings attention to the increasingly critical role of data quality, selection, and synthesis in achieving high model performance with reduced computational cost. Rather than focusing on ever-larger datasets and models, CDEL emphasizes the curation and distillation of high-value data—leveraging techniques such as dataset distillation, data pruning, synthetic data generation, and sampling optimization. These approaches aim to reduce redundancy, improve generalization, and enable learning in data-scarce regimes. The workshop will bring together researchers and practitioners from vision, language, and multimodal learning to share insights and foster collaborations around efficient, scalable, and sustainable data-driven machine learning.