Adaptive Training

Adaptive training methods aim to optimize the training process of various machine learning models, improving efficiency and performance by dynamically adjusting parameters or data selection strategies throughout training. Current research focuses on developing adaptive algorithms for large language models (LLMs), physics-informed neural networks (PINNs), and graph neural networks (GNNs), often incorporating techniques like self-paced learning, dynamic difficulty measurement, and modular training approaches. These advancements enhance model accuracy, reduce computational costs, and improve robustness, impacting fields ranging from natural language processing and scientific computing to medical diagnosis and robotic rehabilitation. The ultimate goal is to create more efficient and effective training paradigms for diverse applications.