Neural Scaling Law

Neural scaling laws describe how the performance of deep neural networks improves with increases in model size, training data, and computational resources. Current research focuses on refining theoretical understanding of these scaling relationships, particularly investigating the interplay between these factors and exploring how architectural choices (e.g., modularity, feature learning) and training methods (e.g., stochastic gradient descent, adaptive sampling) affect scaling behavior across diverse tasks and model types (including transformers, graph neural networks, and those used in embodied AI). These laws are crucial for optimizing resource allocation in deep learning, guiding the design of more efficient and effective models, and improving our fundamental understanding of the learning process itself.