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 offers strong performance but suffers from severe training instability. In this work, we propose SpanNorm, a novel technique designed to resolve this dilemma by integrating the st
The continuous drive for more performant and stable large language models (LLMs) requires overcoming fundamental architectural trade-offs.
This research addresses a core challenge in deep Transformer architectures, potentially unlocking greater scale and efficiency for the next generation of AI models.
The ability to train deeper and more stable Transformers without sacrificing performance could lead to more capable and reliable AI systems.
- · AI developers
- · Hyperscalers
- · AI research institutions
- · Developers reliant on unstable 'PostNorm' architectures
- · Systems with high inference costs due to inefficient models
Increased pace of large language model development and deployment.
Reduced compute costs for training extremely deep models, democratizing access to powerful AI architectures.
Acceleration of AI agent capabilities as underlying model performance improves across scaling laws.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL