Papers › Learning to Prove Theorems by Learning to Generate Theorems

Learning to Prove Theorems by Learning to Generate Theorems

17 Feb 2020NeurIPS 2020 12arXiv:2002.07019archive 2025-07-28

Mingzhe Wang, Jia Deng

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.

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