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Weibull framework reveals AdamW training dynamics in transformers

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the evolution of weight-scale parameters in transformer models during AdamW training. The study derives a three-force decomposition of the squared weight norm, identifying alignment, injection, and decay forces as key drivers. Analysis of Pythia-70M models indicates that alignment force is dominant during the weight-scale growth phase, while alignment and decay forces balance near saturation, leading to relaxation. The researchers also developed a spline displacement method to accurately recover alignment force from sparse checkpoints. AI

IMPACT Provides a deeper understanding of transformer training dynamics, potentially leading to more efficient model optimization techniques.

RANK_REASON The cluster contains a research paper detailing novel analysis of transformer training dynamics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Weibull framework reveals AdamW training dynamics in transformers

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The cluster contains a research paper detailing novel analysis of transformer training dynamics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 Deutsch(DE) · Tiexin Ding ·

    Weibull Weight-Scale Parameter Evolution under AdamW Training Dynamics

    arXiv:2606.19367v1 Announce Type: new Abstract: Building on a two-parameter Weibull framework for diagnosing transformer weight distributions, we study why the Weibull weight-scale parameter $\lambda$ grows, overshoots, and then relaxes during AdamW training. We derive a leading-…

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