Author

Artidoro Pagnoni

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#artificial intelligence Preprint Oct 2026

How to Loop Your Transformer: Loop Scaling, Specialization, and Parallel Inference

Looping in transformer models reuses weights by repeatedly applying the same transformer, or parts of it, making the model deeper without increasing its parameter count. Whether looping is useful depends on which constraint dominates: model size, training compute, or autoregressive inference efficiency. We study these...

C. Ekbote, Artidoro Pagnoni, Devendra Singh Sachan et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Breaking the Token Ceiling: Distilling Smaller, Stronger Byte Models

Small models are made more capable through distillation from a larger one that shares their tokenization scheme. However, do distilled byte and token models behave similarly in terms of scaling trends as compute and data increases? To enable this comparison, we introduce two variants to efficiently convert token logits...

Kalyani Marathe, Artidoro Pagnoni, Tomasz Limisiewicz et al. · 0 citations