The paper explores the impact of parameter sparsity on the scaling behavior of Transformers trained on massive datasets. It identifies a scaling law that describes the relationship between weight sparsity, number of non-zero parameters, and amount of training data. The findings provide insights into the optimal sparsity level for computational efficiency improvements.

https://arxiv.org/abs//2309.08520

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