  In this work, we empirically show that updating pretrained LMs (350M, 1.3B,
2.7B) with just a few steps of Gradient Ascent Post-training (GAP) on random,
unlabeled text corpora enhances its zero-shot generalization capabilities
across diverse NLP tasks. Specifically, we show that GAP can allow LMs to
become comparable to 2-3x times larger LMs across 12 different NLP tasks. We
also show that applying GAP on out-of-distribution corpora leads to the most
reliable performance improvements. Our findings indicate that GAP can be a
promising method for improving the generalization capability of LMs without any
task-specific fine-tuning.
