Large Scale Training

Large-scale training focuses on efficiently training massive machine learning models, often with billions of parameters, across distributed computing systems. Current research emphasizes techniques to reduce memory consumption (e.g., layerwise importance sampling), improve communication efficiency (e.g., communication-computation overlap, 0/1 Adam), and optimize training speed (e.g., active learning, model parallelism) for various architectures including transformers, graph neural networks, and GANs. These advancements are crucial for developing powerful models in diverse fields like natural language processing, medical imaging, and recommender systems, ultimately impacting the performance and accessibility of AI applications.