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We consider the task of automated theorem proving, a key AI task. Deeplearning has shown promise for training theorem provers, but there are limitedhuman-written theorems and proofs available for supervised learning. To addressthis limitation, we propose to learn a neural generator that automaticallysynthesizes theorems and proofs for the purpose of training a theorem prover.Experiments on real-world tasks demonstrate that synthetic data from ourapproach improves the theorem prover and advances the state of the art ofautomated theorem proving in Metamath. Code is available athttps://github.com/princeton-vl/MetaGen.

Learning to Prove Theorems by Learning to Generate Theorems