The paper studies how Adam’s two momentum timescales, β1 and β3, influence loss spikes during neural‑network training. By mapping training dynamics across the (β1,β3) plane, the authors find an approximately linear boundary, 1-β3 = C(1-β1), that separates spiky from non‑spiky behavior, with the coefficient C linked to the effective loss exponent in superquadratic loss functions. They also show that confident cross‑entropy losses create a core–wall landscape that behaves superquadratically at the scale of an optimizer update, explaining the observed spikes.
By Gaoxiang Tang, Huanran Chen, Ziming Liu
arXiv:2507. 14056v3 Announce Type: replace-cross Abstract: Recent work in continual learning has highlighted the stability gap -- a temporary performance drop on previously learned tasks when new ones are introduced.
By Alejandro Rodriguez-Garcia, Anindya Ghosh, Srikanth Ramaswamy
arXiv:2606. 14259v1 Announce Type: new Abstract: Prior work has identified several factors that can contribute to the performance gap between Adam and SGD, spanning data aspects, architecture design, and optimization properties.
By Chenxiang Zhang, Rustem Islamov, Enea Monzio Compagnoni, Jun Pang, Aurelien Lucchi, Antonio Orvieto
arXiv:2604. 22407v2 Announce Type: replace Abstract: Many continual-learning methods modify gradients upstream (e.
By Yuelin Hu, Zhenbo Yu, Zhengxue Cheng, Wei Liu, Li Song
arXiv:2601. 18699v2 Announce Type: replace Abstract: Sequential fine-tuning of Large Language Models (LLMs) adaptation to target tasks often triggers catastrophic forgetting, where the acquisition of novel target skills degrades ancestral capabilities.
By Gustav Olaf Yunus Laitinen-Fredriksson Lundstrom-Imanov
arXiv:2607. 09967v1 Announce Type: cross Abstract: Many neural networks operations have a multiplicative nature rather than additive: halving or doubling a norm are analogous relatively but require unequal optimization distances when taking linear steps.
By Ethan Smith