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arXiv · 2610.27758

AC/DC on a Budget - Alternating Sparse Phases

Rahul Nittala·Advait Gadhikar·Tom Jacobs·Rebekka Burkholz
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Abstract

State-of-the-art neural network sparsification relies on alternating dense and sparse training phases. While computationally expensive, dense phases are critical for performance. Our empirical analysis reveals the mechanics behind this gain: upon reintroduction, previously masked parameters receive disproportionately large gradients that drive essential mask rewiring and parameter sign-flipping. Building on these insights, we optimize the performance-to-FLOPs Pareto frontier through three innovations: (1) zero-initializing masked parameters during dense phases to promote relevant sign flips, (2) shortening dense phases, and (3) replacing the dense phase entirely with a sparse rewiring phase that updates only the active mask and a small, high-gradient fraction of out-of-mask weights. Evaluated on CIFAR100 and ImageNet, our variant ARC (Alternating Rewiring Compression) matches baseline AC/DC performance at a fraction of the computational cost, establishing a superior performance-to-FLOPs frontier also compared to standard sparse-to-sparse algorithms. Experiments with GPT-2 on OpenWebText further show improvements in validation perplexity and average zero-shot performance

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Rahul Nittala, Advait Gadhikar, Tom Jacobs, Rebekka Burkholz. 2026-10-20. AC/DC on a Budget - Alternating Sparse Phases. https://arxiv.org/abs/2610.27758

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