Model selection in active learning
There are nested loops in training machine learning models. The outer loop is selecting the hyperparameters for the model, including the architecture. The inner loop trains the model's parameters while holding the hyperparameters constant.
It is possible to try many possible options on outer loop and make those options dependent on the results of the inner loop.