This research provides a comprehensive analytical foundation for understanding how different Layer Normalization strategies influence Transformer training dynamics, especially for Large Language Models.

Key findings:

• Traditional Pre-LN and Post-LN approaches have inherent limitations for large-scale training
• The paper introduces new strategies that better balance training stability and convergence speed
• Analysis provides clear insights into how normalization affects gradient flow in deep transformer networks
• Results offer practical engineering solutions for more efficient LLM development

For ML engineers, this research matters because it directly addresses core architecture challenges in building robust, efficient language models, potentially reducing computing requirements while improving model quality.

Peri-LN: Revisiting Layer Normalization in the Transformer Architecture