Iterative Training

Iterative training refines models by repeatedly updating parameters based on feedback from previous iterations, aiming to improve performance and address limitations of single-pass training. Current research focuses on applying iterative methods to diverse models, including large language models (LLMs), diffusion models, and neural networks for various tasks like optimization, audio enhancement, and image generation, often incorporating techniques like multi-armed bandits, reinforcement learning, and self-supervised learning to guide the iterative process. This approach is significant because it enables more efficient model optimization, enhanced robustness, and improved generalization capabilities across various domains, leading to advancements in areas such as natural language processing, computer vision, and robotics.