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New research details logistic gradient descent paths with large initializations

By PulseAugur Editorial · [1 sources] ·

A new paper explores the geometric approximation of logistic gradient descent trajectories, particularly when initialized from a previously trained model at a large scale. The research introduces a continuous ideal path composed of linear segments, which includes a negative-margin correction phase followed by minimum-margin growth. This approximation converges to the full-batch logistic gradient descent trajectory as the initialization scale increases, providing quantitative error bounds and asymptotic formulas for peak and cumulative training losses. AI

IMPACT Provides theoretical insights into model training dynamics, potentially informing future optimization strategies.

RANK_REASON The cluster contains a single academic paper published on arXiv. [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 →

New research details logistic gradient descent paths with large initializations

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The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Junjie Xiao, Huiwen Jia ·

    Ideal Paths for Approximating Logistic Gradient Descent Trajectories at Large Initialization

    arXiv:2610.04142v2 Announce Type: replace Abstract: Modern training on a new task often starts from a previously trained model rather than from scratch, raising the question of how this initialization affects the subsequent training trajectory. Classical implicit-bias results cha…

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