  We consider the task of automated theorem proving, a key AI task. Deep
learning has shown promise for training theorem provers, but there are limited
human-written theorems and proofs available for supervised learning. To address
this limitation, we propose to learn a neural generator that automatically
synthesizes theorems and proofs for the purpose of training a theorem prover.
Experiments on real-world tasks demonstrate that synthetic data from our
approach improves the theorem prover and advances the state of the art of
automated theorem proving in Metamath. Code is available at
https://github.com/princeton-vl/MetaGen.
