Adam optimizer's loss spikes linked to internal dynamics

Researchers have identified a key mechanism behind the loss spikes that frequently occur during neural network training with the Adam optimizer. Their analysis reveals that these spikes are not solely due to landscape geometry but stem from the internal dynamics of Adam's second moment estimator. Specifically, a decoupling between the adaptive preconditioner and instantaneous squared gradients causes the preconditioner to decay autonomously, leading to instability and dramatic loss increases. AI

IMPACT Identifies a root cause for training instability, potentially leading to more robust optimization methods for large-scale models.

RANK_REASON Academic paper detailing a novel mechanism for a common phenomenon in neural network training. [lever_c_demoted from research: ic=1 ai=1.0]

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• Adam
• Zhiwei Bai

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