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.
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.
arXiv:2606. 29158v1 Announce Type: cross Abstract: Learning-rate transfer can reduce the cost of training large language models: instead of sweeping learning rates at target scale, practitioners extrapolate from smaller runs.
arXiv:2609.37076v1 Announce Type: new Abstract: Large language models trained on vast corpora inherently risk memorizing harmful content that may later re-emerge in their outputs. To mitigate this is...
arXiv:2602.03702v2 Announce Type: replace-cross Abstract: Large language models are increasingly trained in continual or open-ended settings, where the total training horizon is not known in advance....
arXiv:2606. 06888v1 Announce Type: new Abstract: Classical scaling laws for language model pretraining balance model size against training dataset size under a fixed compute budget, assuming abundant data and a single pass over the corpus.
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.
The paper reviews data strategies for large language models, emphasizing that data strategy—selecting, weighting, and scheduling training data—determines model capabilities within a fixed data budget. It treats high‑quality data as having training‑dependent utility, varying with the target objective, model, and training state. The authors organize existing methods by decision granularity, model dependence, and temporal adaptivity, comparing their computational costs and empirical support, and note limited evidence for transfer across model scales and end‑to‑end cost parity.
arXiv:2605. 29223v3 Announce Type: replace Abstract: The parameter counts of the most widely used large language models (LLMs) are often withheld by their developers, leaving model size -- a primary reference point for interpreting capabilities and costs -- largely undisclosed.
The paper introduces SynPro, a synthetic data generation framework that augments limited organic text for large language model pretraining by applying rephrasing and reformatting operations. SynPro’s generators are optimized with reinforcement learning rewards for quality, faithfulness, and data influence, and are updated continuously as training plateaus. Experiments on 400M, 1.1B, and 2B models show that SynPro can unlock 3.4–5.2× the effective tokens of standard repetition, even outperforming a non‑data‑bound oracle at larger scales.
arXiv:2608. 14071v1 Announce Type: new Abstract: As large language models scale, their training-token budgets must also increase to maintain an appropriate tokens-per-parameter ratio (\(\mathrm{TPP}\)).
The paper investigates the often-overlooked scale vectors in large language models, showing that despite their tiny size they are crucial for pre‑training performance. The authors provide theoretical insights that scale vectors mainly aid optimization rather than expressivity, and they analyze how weight decay affects different normalization layers. Building on these findings, they propose lightweight improvements—branch‑specific heterogeneity, better placement, and magnitude‑direction reparameterization—that consistently reduce loss across a range of model sizes and training settings.
arXiv:2607.09885v3 Announce Type: replace Abstract: We present Index-1.9B, a series of open small language models developed at Bilibili. The series comprises four models: Index-1.9B-Base, a foundatio...