Provable Edge-of-Stability for Adam on a One-Dimensional Quadratic
Researchers have studied how the Adam optimization algorithm behaves near a point where it stops improving. They focused on a simple mathematical problem to understand why this happens and found that in many cases, Adam tends to move back towards a stable state. However, they also discovered situations where this doesn't happen, leading to periodic or divergent behavior.
Researchers have studied how the Adam optimization algorithm behaves near a point where it stops improving. They focused on a simple mathematical problem to understand why this happens and found that in many cases, Adam tends to move back towards a stable state. However, they also discovered situations where this doesn't happen, leading to periodic or divergent behavior.
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Why it matters: This research is important for AI engineers because it sheds light on the dynamics of popular optimization algorithms like Adam, which are widely used in deep learning. Understanding these dynamics can help improve the stability and efficiency of training processes.
Source: https://arxiv.org/abs/2608.20638
This article was originally published at: https://arxiv.org/abs/2608.20638