arXiv:2602.10408v2 Announce Type: replace Abstract: Normalization layers are standard in transformers, but it is not clear whether their sample-dependent computations are necessary throughout both training and inference. This work develops a gated normalization-removal approach for pre-norm transformers. The approach is implemented using TaperNorm, which starts from standard RMSNorm/LayerNorm and gradually tapers to learned sample-independent linear or affine maps. Once the gate reaches zero, per-token statistics are no longer computed in the tapered layers and the resulting maps can be folded
This research addresses a long-standing challenge in transformer architecture, aiming to optimize efficiency and performance as models scale exponentially.
A strategic reader should care because this innovation can lead to more efficient, faster, and potentially smaller AI models, reducing computational overhead for training and inference.
Normalization layers, previously considered standard, may no longer be universally necessary in their current form, leading to a new paradigm in transformer design.
- · AI model developers
- · Cloud computing providers (reduced inference costs)
- · Hardware manufacturers (potential for less intensive hardware)
- · Developers solely reliant on legacy normalization techniques
Transformers become more compute-efficient and potentially faster to train and deploy.
This efficiency could enable larger, more complex models to be run on more constrained hardware or at lower operational costs.
Increased accessibility and reduced cost of advanced AI could accelerate innovation across various applications, potentially boosting AI adoption in power-constrained environments or edge devices.
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Read at arXiv cs.LG