Several challenges make it difficult for sparse neural networks to compete with dense models. First, setting a large fraction of weights
to zero impairs forward and gradient signal propagation.
Second, sparse studies often need to test multiple sparsity levels, while also introducing new hyperparameters (HPs) , leading to prohibitive tuning costs.
Indeed, the standard practice is to
re-use the learning HPs originally crafted for dense models. Unfortunately, we show sparse and
dense networks do not share the same optimal HPs.
Without stable dynamics and effective training recipes, it is
costly to test sparsity at scale, which is key to surpassing dense networks and making the business case for
sparsity acceleration in hardware. A holistic approach is needed to tackle these
challenges and we proposesparse maximal update parameterization(SµPar) 
as one such approach.SµParensures activations, gradients, and weight updates all scale independently of sparsity level.
Further, by reparameterizing the HPs,SµParenables the same HP values to be optimal as we vary both sparsity level and model width.
HPs can be tuned on small dense networks and transferred to large sparse models, greatly reducing tuning costs.
On large-scale language modeling,SµPartraining improves loss by up to 8.2% over the common approach of using the dense model standard parameterization.