We present a reinforcement learning (RL) based guidance system for automated theorem proving geared towards Finding Longer Proofs (FLoP). Unlike most learning based approaches, we focus on generalising from very little training data and achieving near complete confidence. We use several simple, structured datasets with very long proofs to show that FLoP can successfully generalise a single training proof to a large class of related problems. On these benchmarks, FLoP is competitive with strong theorem provers despite using very limited search, due to its ability to solve problems that are prohibitively long for other systems.
@article{arxiv.1905.13100,
title = {Towards Finding Longer Proofs},
author = {Zsolt Zombori and Adrián Csiszárik and Henryk Michalewski and Cezary Kaliszyk and Josef Urban},
journal= {arXiv preprint arXiv:1905.13100},
year = {2021}
}