Optimal Training

Optimal training in machine learning focuses on maximizing model performance and efficiency by tuning training parameters and architectures. Current research emphasizes improving reliability in large-scale model training (e.g., for LLMs), exploring efficient parallelization strategies for reduced cost and power consumption, and developing methods to leverage existing model knowledge for improved adaptation to new data or tasks (e.g., using geometry-aware adaptation or optimal condition training). These advancements are crucial for addressing the computational challenges and improving the accuracy and robustness of various machine learning applications, ranging from time-series forecasting to image recognition and natural language processing.