Author

Mike Lewis

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#artificial intelligence Preprint Oct 2026

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

The results show that effective looping requires matching the design to the resource constraint and inference setting, and that preserving the current-token embedding and separately normalizing it and the previous-token hidden state before combining them recovers strong performance across model scales and training budg...

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. · 1 citation

Preprint Aug 2026

Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes

This study provides empirical clarity through a systematic exploration of multimodal pretraining and derives efficient pretraining recipes that achieve strong generative performance using only 5% of the compute budget.

Junlin Han, Shengbang Tong, David Fan et al. · 0 citations