  Implicit deep learning has recently gained popularity with applications
ranging from meta-learning to Deep Equilibrium Networks (DEQs). In its general
formulation, it relies on expressing some components of deep learning pipelines
implicitly, typically via a root equation called the inner problem. In
practice, the solution of the inner problem is approximated during training
with an iterative procedure, usually with a fixed number of inner iterations.
During inference, the inner problem needs to be solved with new data. A popular
belief is that increasing the number of inner iterations compared to the one
used during training yields better performance. In this paper, we question such
an assumption and provide a detailed theoretical analysis in a simple setting.
We demonstrate that overparametrization plays a key role: increasing the number
of iterations at test time cannot improve performance for overparametrized
networks. We validate our theory on an array of implicit deep-learning
problems. DEQs, which are typically overparametrized, do not benefit from
increasing the number of iterations at inference while meta-learning, which is
typically not overparametrized, benefits from it.
