arXiv AI
Anytime Pretraining: Horizon-Free Learning-Rate Schedules with Weight Averaging
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Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling
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.
arXiv Machine Learning
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WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training
arXiv:2607. 10959v1 Announce Type: new Abstract: Standard learning rate schedules such as cosine annealing are tied to a fixed training horizon, limiting their ability to accommodate post hoc horizon extension.
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Training for the Model You Return: Improving Optimization for Iterate-Averaged Language Models
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Deriving Scaling Laws for OpenEuroLLM Models: Learning Rate, Batch Size and Loss
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arXiv AI
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q0: Primitives for Hyper-Epoch Pretraining
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arXiv AI
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Demystifying Training-Time Augmentation for Data-Constrained Language Model Pretraining
The paper investigates training-time data augmentation as a regularizer for autoregressive language model pretraining in data‑constrained, compute‑abundant settings. It introduces three orthogonal augmentation categories—token‑level noise, sequence permutations, and target offset prediction—and shows through systematic ablations that each category delays overfitting and reduces validation loss, with random token replacement performing best individually. Combining augmentation categories further lowers the minimum validation loss, demonstrating that such augmentations mitigate data inefficiency in autoregressive pretraining.