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A major challenge in applying machine learning to automated theorem provingis the scarcity of training data, which is a key ingredient in trainingsuccessful deep learning models. To tackle this problem, we propose an approachthat relies on training with synthetic theorems, generated from a set ofaxioms. We show that such theorems can be used to train an automated prover andthat the learned prover transfers successfully to human-generated theorems. Wedemonstrate that a prover trained exclusively on synthetic theorems can solve asubstantial fraction of problems in TPTP, a benchmark dataset that is used tocompare state-of-the-art heuristic provers. Our approach outperforms a modeltrained on human-generated problems in most axiom sets, thereby showing thepromise of using synthetic data for this task.

Learning to Prove from Synthetic Theorems