Author

Tijmen Blankevoort

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Jul 2026

SOAP, Muon, and Beyond: Pushing LLM Pretraining Scales

This work adapt and enhance preconditioned gradient methods to overcome the practical challenges of large-scale LLM pretraining and proposes algorithmic modifications including per-step QR orthogonalization and improved preconditioning strategies to enable stable training in these regimes.

Mikail Khona, Aditya Vavre, Boxiang Wang et al. · arXiv.org · 0 citations

UvA-DARE (Digital Academic Repository) Elastic ViTs from Pretrained Models without Retraining

SnapViT: single-shot network approximation for pruned Vision Transformers is introduced, a new post-pretraining structured pruning method that enables elastic inference across a continuum of compute budgets, and a self-supervised importance scoring mechanism that maintains strong performance without requiring retraining or labels.

Walter Simoncini, Michael Dorkenwald, Tijmen Blankevoort et al. · 0 citations