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Adam optimizer's tied momentum parameters reveal unique training dynamics

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv explores the dynamics of the Adam optimizer, a core component in large-scale AI training. The research identifies a specific mechanism related to its two momentum parameters, $\beta_1$ and $\beta_2$. The study demonstrates that when these parameters are tied ($\beta_1 = \beta_2$), a lag term in the update coordinate vanishes, leading to sign-dominated updates and smoother training trajectories. This finding offers a mechanistic explanation for why tied momentum configurations are dynamically distinctive and can maintain strong performance. AI

IMPACT Provides a deeper understanding of optimization techniques crucial for training large-scale AI models.

RANK_REASON The cluster contains a research paper detailing novel findings about an AI optimization algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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Adam optimizer's tied momentum parameters reveal unique training dynamics

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The cluster contains a research paper detailing novel findings about an AI optimization algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Alberto Fern\'andez-Hern\'andez, Cristian P\'erez-Corral, Jose I. Mestre, Manuel F. Dolz, Enrique S. Quintana-Ort\'i ·

    Why $\beta_1 = \beta_2$ Is Dynamically Special in Adam

    arXiv:2601.21739v3 Announce Type: replace-cross Abstract: Adam has been at the core of large-scale training for almost a decade, yet the role of its two momentum parameters remains poorly understood. Recent work shows that tying $\beta_{1}=\beta_{2}$ can preserve Adam's strong pe…

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