Training Trajectory

Training trajectory analysis focuses on understanding how model parameters evolve during training, aiming to improve efficiency, stability, and generalization performance. Current research explores diverse approaches, including optimizing update rules (e.g., using nowcasting networks), improving data selection strategies (e.g., leveraging small model trajectories for large model fine-tuning), and developing novel training algorithms (e.g., annealed multiple choice learning and variance-controlled adaptive sampling). These advancements have implications for reducing computational costs, enhancing model accuracy, and providing deeper insights into the dynamics of deep learning, ultimately impacting the development and deployment of more efficient and effective machine learning models.