A large model is being trained using a combined objective. This objective includes a 'distillation loss,' which encourages the large model to mimic the outputs of a smaller, weaker 'teacher' model. It also includes a 'supervised loss,' which is calculated against a set of known correct answers (ground-truth). What is the primary analytical reason for including the 'supervised loss' in this training process?

A large model is being trained using a combined objective that incorporates signals from both ground-truth data and a smaller 'teacher' model. Based on a typical diagram of this process, arrange the following computational steps into the correct logical order for a single training update.

Diagnosing Training Imbalance