Neural Networks (NNs) struggle to efficiently learn certain problems, such as parity problems, even when there are simple learning algorithms for those problems. Can NNs discover learning algorithms on their own? We exhibit a NN architecture that, in polynomial time, learns as well as any efficient learning algorithm describable by a constant-sized learning algorithm. For example, on parity problems, the NN learns as well as row reduction, an efficient algorithm that can be succinctly described.…