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New rod flow model tracks Adam optimizer at edge of stability

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new "rod flow" model to better understand how adaptive gradient optimization methods, like Adam, operate at the edge of stability. This model extends previous work on gradient descent to include momentum-based methods, treating optimization iterates as a one-dimensional "rod." The new framework accurately tracks discrete iterates for eight different optimizers, including Adam and RMSProp, across various machine learning architectures. AI

IMPACT Provides a more accurate theoretical framework for understanding and potentially improving the stability of common optimization algorithms used in machine learning.

RANK_REASON The cluster contains an academic paper detailing a new modeling technique for optimization algorithms.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New rod flow model tracks Adam optimizer at edge of stability

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The cluster contains an academic paper detailing a new modeling technique for optimization algorithms.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Eric Regis, Sinho Chewi ·

    A Rod Flow Model for Adam at the Edge of Stability

    arXiv:2605.06821v1 Announce Type: cross Abstract: Cohen et al. (arXiv:2207.14484) observed that adaptive gradient methods such as Adam operate at the edge of stability. While there has been significant work on continuous-time modeling of gradient descent at the edge of stability,…

  2. arXiv stat.ML TIER_1 English(EN) · Sinho Chewi ·

    A Rod Flow Model for Adam at the Edge of Stability

    Cohen et al. (arXiv:2207.14484) observed that adaptive gradient methods such as Adam operate at the edge of stability. While there has been significant work on continuous-time modeling of gradient descent at the edge of stability, extending these models to momentum methods remain…

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