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Neoteric works have shown that modern deep learning models can exhibit asparse double descent phenomenon. Indeed, as the sparsity of the modelincreases, the test performance first worsens since the model is overfittingthe training data; then, the overfitting reduces, leading to an improvement inperformance, and finally, the model begins to forget critical information,resulting in underfitting. Such a behavior prevents using traditional earlystop criteria. In this work, we have three key contributions. First, we proposea learning framework that avoids such a phenomenon and improves generalization.Second, we introduce an entropy measure providing more insights into theinsurgence of this phenomenon and enabling the use of traditional stopcriteria. Third, we provide a comprehensive quantitative analysis of contingentfactors such as re-initialization methods, model width and depth, and datasetnoise. The contributions are supported by empirical evidence in typical setups.Our code is available at https://github.com/VGCQ/DSD2.

DSD$^2$: Can We Dodge Sparse Double Descent and Compress the Neural Network Worry-Free?