SpanNorm: Reconciling Training Stability and Performance in Deep Transformers 事件

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers arXiv:2601.22580v2 Announce Type: replace Abstract: The success of Large Language Models (LLMs) hinges on the stable training of deep Transformer architectures. A critical design choice is the placement of normalization layers, leading to a fundamental trade-off: the ``PreNorm'' architecture ensures training stability at the cost of potential performance degradation in deep models, while the ``PostNorm'' architecture