  Curriculum learning (CL) aims to increase the performance of a learner on a
given task by applying a specialized learning strategy. This strategy focuses
on either the dataset, the task, or the model. There is little to no work
analysing the possibilities to apply CL on the model capacity in natural
language processing. To close this gap, we propose the cup curriculum. In a
first phase of training we use a variation of iterative magnitude pruning to
reduce model capacity. These weights are reintroduced in a second phase,
resulting in the model capacity to show a cup-shaped curve over the training
iterations. We empirically evaluate different strategies of the cup curriculum
and show that it outperforms early stopping reliably while exhibiting a high
resilience to overfitting.
