arXiv:2607. 27731v1 Announce Type: new Abstract: Modern deep learning typically keeps the batch size static throughout training, thus overlooking the joint effect of learning rate and batch size on the training dynamics.
By Jiaxiang Li, Zhiqi Bu, Shiyun Xu
arXiv:2607. 25271v1 Announce Type: cross Abstract: Classical compute-optimal scaling laws assume an unbounded supply of fresh pretraining data, yet pretraining is increasingly entering a regime in which compute grows faster than the availability of high-quality data.
By Tian Qin, Kimia Hamidieh, David Alvarez-Melis
Classical compute-optimal scaling laws assume an unbounded supply of fresh pretraining data, yet pretraining is increasingly entering a regime in which compute grows faster than the availability of high-quality data. We propose Compute-Data (CD) scaling laws, a unified framework that bridges compute-optimal scaling, where data scales freely with compute, and data-optimal scaling, where the corpus is fixed while compute can grow without bound.
arXiv:2605. 24316v3 Announce Type: replace Abstract: Mini-batching is central to large-scale optimization, yet its role in statistical scaling laws remains limited.
By Ziyan Chen, Zhongzhu Zhou, Ding-Xuan Zhou
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
The paper introduces a User Behavioral Densing Law that quantifies how the minimum sufficient tokenization capacity scales with data size in user representation learning. A pilot study on a billion‑scale Alipay dataset shows raw data scaling bottlenecks and the benefits of tokenization, while theoretical analysis and experiments reveal an approximately linear relationship between the logarithms of tokenization capacity and input data size. Using this law, the authors develop ALGN, an adaptive variable‑length tokenization method that outperforms existing baselines across diverse data sources and downstream tasks.
By Bin Dou, Junru Zhang, Zhaoyi Yuan, Wuliang Huang, Letian Gong, Baokun Wang, Huan Li, Yu Cheng, Weiqiang Wang