Optimizing a Student Model's Training

An engineer is training a student language model using a combined objective that balances learning from a teacher model's predictions (distillation loss) and learning from the ground-truth data (standard loss). The interpolation coefficient, λ, weighs the teacher's influence. The engineer observes that the student model quickly learns to mimic the teacher's output, but its performance on a validation set eventually plateaus and fails to surpass the teacher's performance, even though the student

A student model is being trained using a combined objective that includes a term for learning from a teacher model, weighted by a coefficient λ. Arrange the following training stages in the order that corresponds to a typical and effective dynamic adjustment schedule for λ, from the highest value of λ to the lowest.