Author

Niccolò Ajroldi

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#artificial intelligence Preprint Sep 2026

Amortizing Scaling Law Construction Costs

Scaling laws guide the design choices for training large foundation models, but deriving them involves training an exhaustive grid over hyperparameters, token budgets, and parameter counts, which is computationally expensive. Fitting a scaling law, however, only requires the best-loss frontier across compute scales, di...

Abhash Kumar Jha, Diana-Alexandra Onutu, Neeratyoy Mallik et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Deriving Scaling Laws for OpenEuroLLM Models: Learning Rate, Batch Size and Loss

This study establishes a first baseline and scaling procedure for the development of future OpenEuroLLM models, and characterize the dependence of loss on model capacity and dataset size, evaluating recently proposed scaling forms that explicitly model their interaction.

Niccolò Ajroldi, Diana-Alexandra Onutu, Haider Al-Tahan et al. · 1 citation