1 code implementation • 3 Feb 2022 • Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, Ilya Sutskever
We explore the use of expert iteration in the context of language modeling applied to formal mathematics.
Ranked #4 on Automated Theorem Proving on miniF2F-test (using extra training data)
no code implementations • 11 Oct 2021 • Jesse Michael Han, Igor Babuschkin, Harrison Edwards, Arvind Neelakantan, Tao Xu, Stanislas Polu, Alex Ray, Pranav Shyam, Aditya Ramesh, Alec Radford, Ilya Sutskever
We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models.
3 code implementations • ICLR 2022 • Kunhao Zheng, Jesse Michael Han, Stanislas Polu
We present miniF2F, a dataset of formal Olympiad-level mathematics problems statements intended to provide a unified cross-system benchmark for neural theorem proving.
Ranked #1 on Automated Theorem Proving on miniF2F-valid (using extra training data)
4 code implementations • ICLR 2022 • Jesse Michael Han, Jason Rute, Yuhuai Wu, Edward W. Ayers, Stanislas Polu
Labeled data for imitation learning of theorem proving in large libraries of formalized mathematics is scarce as such libraries require years of concentrated effort by human specialists to be built.
Ranked #8 on Automated Theorem Proving on miniF2F-test
no code implementations • 7 Sep 2020 • Stanislas Polu, Ilya Sutskever
We explore the application of transformer-based language models to automated theorem proving.
Ranked #1 on Automated Theorem Proving on Metamath set.mm