Norm-matched zeroth-order adaptation preserves the isotropic retention floor while contracting only the anisotropic component, producing a quadratic forgetting gap that favors ZO precisely when the first-order direction has above-average retention curvature.
Elasticzo: A memory-efficient on-device learning with combined zeroth- and first-order optimization
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 2roles
background 2polarities
background 2representative citing papers
Position paper claiming that distributed training across massive edge devices can overcome data depletion and centralized compute monopolies in LLM scaling.
citing papers explorer
-
Why Zeroth-Order Adaptation May Forget Less: A Randomized Shaping Theory
Norm-matched zeroth-order adaptation preserves the isotropic retention floor while contracting only the anisotropic component, producing a quadratic forgetting gap that favors ZO precisely when the first-order direction has above-average retention curvature.
-
Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices
Position paper claiming that distributed training across massive edge devices can overcome data depletion and centralized compute monopolies in LLM scaling.