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AdamW optimizer's memory effects analyzed in new research paper

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to analyze the delayed effects of minibatch perturbations in the AdamW optimizer. By modeling AdamW as a finite-horizon input-state-output system, they can map localized gradient changes to their future impact on training loss. This approach reveals how the optimizer's memory influences the magnitude, timing, and sign of these effects, offering a way to decompose errors and approximate future influences. AI

IMPACT Provides a theoretical framework for understanding and potentially improving the stability and performance of deep learning optimizers.

RANK_REASON The item is a research paper submitted to arXiv detailing theoretical analysis and experimental validation of an optimization algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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AdamW optimizer's memory effects analyzed in new research paper

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The item is a research paper submitted to arXiv detailing theoretical analysis and experimental validation of an optimization algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kang Liu, Suyan Li ·

    Finite-Horizon Input-Output Dynamics of Minibatch Perturbations in AdamW

    arXiv:2608.19762v1 Announce Type: cross Abstract: A minibatch can influence training beyond the update at which it is observed because AdamW stores past gradient information in its optimizer states. We study this delayed effect through paired trajectories that differ only in one …

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