  State-of-the-art language models can match human performance on many tasks,
but they still struggle to robustly perform multi-step mathematical reasoning.
To diagnose the failures of current models and support research, we introduce
GSM8K, a dataset of 8.5K high quality linguistically diverse grade school math
word problems. We find that even the largest transformer models fail to achieve
high test performance, despite the conceptual simplicity of this problem
distribution. To increase performance, we propose training verifiers to judge
the correctness of model completions. At test time, we generate many candidate
solutions and select the one ranked highest by the verifier. We demonstrate
that verification significantly improves performance on GSM8K, and we provide
strong empirical evidence that verification scales more effectively with
increased data than a finetuning baseline.

Machine Learning;Computation and Language