Gradient Update

Gradient updates are the core mechanism by which machine learning models learn, adjusting parameters to minimize error. Current research focuses on improving the efficiency and robustness of gradient updates, exploring techniques like gradient re-parameterization, selective updates, and orthogonal gradient projections, often within the context of large language models, federated learning, and deep neural networks. These advancements aim to reduce computational costs, enhance privacy, and improve model performance in various applications, including image processing, natural language processing, and reinforcement learning. The resulting improvements in training speed and efficiency have significant implications for deploying large-scale models and addressing challenges posed by data heterogeneity and privacy concerns.