Training Stage

Training stage optimization in machine learning focuses on improving model performance and efficiency by strategically managing data and algorithms during the learning process. Current research explores diverse approaches, including using separate teacher-student models to leverage deep network capacity without impacting inference speed, adapting large language models to specific domains via unified data formats, and investigating the optimal timing for incorporating diverse data types (e.g., code) to enhance reasoning capabilities. These advancements are significant for improving model accuracy, reducing computational costs, and expanding the applicability of machine learning across various fields, from medical diagnosis to software development.