  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.
