This paper explores the use of active and passive learning, i.e.\ active and passive techniques to infer state machine models of systems, for fuzzing. Fuzzing has become a very popular and successful technique to improve the robustness of software over the past decade, but stateful systems are still difficult to fuzz. Passive and active techniques can help in a variety of ways: to compare and benchmark different fuzzers, to discover differences between various implementations of the same protocol, and to improve fuzzers.