This repository aggregates academic papers on dataset distillation, a ML technique where models trained on a small synthesized dataset achieve strong performance on the original large dataset. It documents foundational works like Wang et al.’s 2018 paper introducing the task and subsequent advances including gradient matching and applications in continual learning, privacy, and neural architecture search.