  Recent automatic curriculum learning algorithms, and in particular
Teacher-Student algorithms, rely on the notion of learning progress, making the
assumption that the good next tasks are the ones on which the learner is making
the fastest progress or digress. In this work, we first propose a simpler and
improved version of these algorithms. We then argue that the notion of learning
progress itself has several shortcomings that lead to a low sample efficiency
for the learner. We finally propose a new algorithm, based on the notion of
mastering rate, that significantly outperforms learning progress-based
algorithms.
