Pre-training efficiency

Topic

Pre-training efficiency refers to the optimization of computational resources, time, and data required to train foundational machine learning models before they are fine-tuned for specific downstream tasks. It focuses on techniques such as data selection, architectural improvements, and curriculum learning to maximize model performance while minimizing the environmental and financial costs of training. Enhancing pre-training data efficiency specifically aims to achieve high-quality representations using fewer training tokens or smaller datasets.