Joint Distillation

Joint distillation in machine learning focuses on efficiently transferring knowledge from larger, more complex "teacher" models to smaller, more resource-friendly "student" models, aiming to improve student performance while reducing computational costs and memory footprint. Current research explores this technique across various architectures, including Transformers, Mixture-of-Experts (MoE) models, and Graph Neural Networks (GNNs), often incorporating strategies like layer-wise distillation, data-free distillation, and cooperative distillation to enhance efficiency and accuracy. This approach holds significant promise for deploying advanced models in resource-constrained environments and accelerating the training process for large-scale machine learning tasks, impacting both research and practical applications.