  Finding the optimal size of deep learning models is very actual and of broad
impact, especially in energy-saving schemes. Very recently, an unexpected
phenomenon, the ``double descent'', has caught the attention of the deep
learning community. As the model's size grows, the performance gets first
worse, and then goes back to improving. It raises serious questions about the
optimal model's size to maintain high generalization: the model needs to be
sufficiently over-parametrized, but adding too many parameters wastes training
resources. Is it possible to find, in an efficient way, the best trade-off? Our
work shows that the double descent phenomenon is potentially avoidable with
proper conditioning of the learning problem, but a final answer is yet to be
found. We empirically observe that there is hope to dodge the double descent in
complex scenarios with proper regularization, as a simple $\ell_2$
regularization is already positively contributing to such a perspective.
