  As optimizers are critical to the performances of neural networks, every year
a large number of papers innovating on the subject are published. However,
while most of these publications provide incremental improvements to existing
algorithms, they tend to be presented as new optimizers rather than composable
algorithms. Thus, many worthwhile improvements are rarely seen out of their
initial publication. Taking advantage of this untapped potential, we introduce
Ranger21, a new optimizer which combines AdamW with eight components, carefully
selected after reviewing and testing ideas from the literature. We found that
the resulting optimizer provides significantly improved validation accuracy and
training speed, smoother training curves, and is even able to train a ResNet50
on ImageNet2012 without Batch Normalization layers. A problem on which AdamW
stays systematically stuck in a bad initial state.
