arXiv:2602.08064v2 Announce Type: replace Abstract: The long-standing tension between Pre- and Post-Norm remains an open problem in Transformer architecture, reflecting a fundamental trade-off between training stability and representational capacity. Prior attempts to combine their strengths have made progress, but often show limited robustness across training settings, restricting their broader applicability. We revisit this dilemma, showing that single-stream architectures struggle to reconcile Pre-Norm's stable identity-gradient propagation with Post-Norm's normalization of the main residua
This research addresses a long-standing architectural challenge in Transformer models, indicating a persistent focus on improving foundational AI building blocks.
Resolving the tension between Pre- and Post-Norm in Transformers can lead to more robust, stable, and generalizable AI models, accelerating progress in various AI applications.
New architectural paradigms could emerge for large language models and other Transformer-based systems, potentially making their development and deployment more efficient and reliable.
- · AI researchers and developers
- · Cloud AI providers
- · Companies building on Transformer models
- · Organizations reliant on less stable or efficient Transformer architectures
Improved stability and capacity of Transformer models.
Faster training times and reduced computational overhead for developing advanced AI.
Accelerated development of more capable and reliable AI agents and systems across various domains.
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Read at arXiv cs.LG