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New research analyzes Transformer stability under layer normalization

By PulseAugur Editorial · [1 sources] ·

A new paper from Kelvin Kan explores the stability of deep Transformers during training, focusing on the placement of layer normalization. The research provides theoretical insights into how different placements affect the growth of hidden states and the backpropagation of gradients. This analysis offers guidance for optimizing Transformer architectures and scaling residual steps for improved stability and performance. AI

IMPACT Provides theoretical guidance for improving the stability and performance of Transformer models.

RANK_REASON Academic paper published on arXiv detailing theoretical analysis of model architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research analyzes Transformer stability under layer normalization

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Academic paper published on arXiv detailing theoretical analysis of model architecture. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kelvin Kan, Xingjian Li, Benjamin J. Zhang, Tuhin Sahai, Stanley Osher, Krishna Kumar, Markos A. Katsoulakis ·

    Stability of Transformers under Layer Normalization

    arXiv:2510.09904v2 Announce Type: replace-cross Abstract: Despite their widespread use, training deep Transformers can be unstable. Layer normalization, a standard component, improves training stability, but its placement has often been ad-hoc. In this paper, we conduct a princip…

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