arxiv
PublishedMay 18, 2026 at 4:00 AM
—neutral
Rethinking Neural Network Learning Rates: A Stackelberg Perspective
Publisher summary· verbatim
arXiv:2605.15530v1 Announce Type: new Abstract: Neural networks are typically trained with a single learning rate across all layers. While recent empirical evidence suggests that assigning layer-specific learning rates can accelerate training, a principled understanding of the conditions and mechani
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivWhen Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems8harxivQuery-Driven Learning for Lifted Reasoning8harxivThe Social World Model Evaluation Gap8harxivConviction Without Evidence: Verdict Stability Is Independent of Visual Grounding in Medical Vision--Language Models8hThe Bubble Brief
MON · WED · FRIRead machine-learning insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗