Task Distillation

Task distillation is a machine learning technique focused on transferring knowledge from a larger, more complex "teacher" model to a smaller, more efficient "student" model, thereby improving the student's performance and reducing computational costs. Current research emphasizes improving distillation across diverse tasks (cross-task distillation), including those with differing architectures (e.g., vision transformers and multi-layer perceptrons) and addressing challenges like catastrophic forgetting in continual learning scenarios. This approach is significant for deploying advanced models in resource-constrained environments and improving the efficiency of training complex models, particularly in areas like natural language processing, computer vision, and robotics.