Training Iteration

Training iteration, the process of repeatedly updating model parameters during machine learning, is a crucial aspect of model development, with current research focusing on optimizing its efficiency and effectiveness. Active areas of investigation include strategies for data selection and scheduling of training iterations to improve model performance and convergence speed, particularly within federated learning and large language model fine-tuning. These optimizations aim to reduce computational costs, enhance model accuracy, and address challenges like catastrophic forgetting and data heterogeneity, ultimately impacting the scalability and practical applicability of various machine learning techniques.