arXiv:2508. 03105v3 Announce Type: replace Abstract: We analyze the convergence behavior of stochastic gradient descent with momentum (SGDM) under dynamic learning-rate and batch-size schedules by introducing a novel and simpler Lyapunov function.
By Yuichi Kondo, Hideaki Iiduka
The paper investigates how learning rate and batch size scale when pretraining dense large language models on English‑prevalent corpora, examining both jointly optimal and marginal evolutions across model capacity and data size. It explores the benefits of a Warmup‑Stable‑Decay learning‑rate schedule, assessing whether optimal hyperparameters transfer between stable and decay phases, and evaluates loss scaling forms that capture interactions between model capacity and dataset size. The study provides a baseline scaling procedure and releases the full set of pretraining runs for future OpenEuroLLM development.
By Niccol\`o Ajroldi, Diana Alexandra Onutu, Haider Al-Tahan, J\"org Franke, Sampo Pyysalo, Jenia Jitsev, Aaron Klein
arXiv:2402.11215v4 Announce Type: replace
Abstract: The choice of batch size in minibatch stochastic gradient optimization is critical for both optimization and generalization performance in large-sc...
By Tim Tsz-Kit Lau, Han Liu, Mladen Kolar
arXiv:2607. 01487v1 Announce Type: new Abstract: We propose a scaling law that takes into account model size and training data while explicitly splitting the latter into training steps and batch size (called three-term law).
By Fabian Schaipp
arXiv:2510. 14717v2 Announce Type: replace-cross Abstract: Increasing the batch size during training -- a ''batch ramp'' -- is a promising strategy to accelerate large language model pretraining.
By Alexandru Meterez, Depen Morwani, Jingfeng Wu, Costin-Andrei Oncescu, Cengiz Pehlevan, Sham Kakade
arXiv:2606. 00888v1 Announce Type: cross Abstract: Dynamic Sparse Training (DST) offers a promising paradigm for improving the training and inference efficiency of deep neural networks; however, we find that in large language model training, DST can suffer from optimization instability, manifested as loss spikes after topology updates.
By Qiao Xiao, Boqian Wu, Patrik Okanovic, Tomasz Sternal, Maurice van Keulen, Elena Mocanu, Mykola Pechenizkiy, Decebal Constantin Mocanu, Torsten Hoefler