For weight sparsity, I know the BitNet 1.58 paper has some claims of improved performance by restricting weights to be either -1, 0, or 1, eliminating the need for multiplying by the weights, and allowing the weights with a value of 0 to be ignored entirely.
Another kind of sparsity, while on the topic is activation sparsity. I think there was an Nvidia paper that used a modified ReLU activation function to make more of the models activations set to 0.
Based on all examples I’ve seen so far in this thread it’s clear there’s no evidence that sparse models actually work better than dense models.
From that perspective, it's disappointing that the paper only enforces modest amounts of activation sparsity, since holding the maximum number of nonzero coefficients constant while growing the number of dimensions seems like a plausible avenue to increase representational capacity without correspondingly higher computation cost.