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arXiv · 2510.22026

Normalization in Attention Dynamics

Nikita Karagodin·Shu Ge·Yury Polyanskiy·Philippe Rigollet
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Abstract

We study the effect of normalization schemes on token representations in deep transformers. Modeling their evolution as interacting particles on the sphere, we show that normalization acts as a form of speed regulation. This perspective enables a unified analysis of several schemes -- including Post-LN, Pre-LN, Mix-LN, Peri-LN, nGPT -- revealing how they influence clustering dynamics and representation collapse. Our framework clarifies how different schemes shape token representations across layers and provides a principled basis for comparing them, identifying Peri-LN as a particularly effective choice.

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Nikita Karagodin, Shu Ge, Yury Polyanskiy, Philippe Rigollet. 2025-10-24. Normalization in Attention Dynamics. https://arxiv.org/abs/2510.22026

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