Stealth Pretraining Seeding plants persistent unsafe behaviors in LLMs via diffuse poisoned web content that activates on precise triggers and evades standard evaluation.
Fixup initialization: Residual learning without normalization.arXiv preprint arXiv:1901.09321
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abstract
Normalization layers are a staple in state-of-the-art deep neural network architectures. They are widely believed to stabilize training, enable higher learning rate, accelerate convergence and improve generalization, though the reason for their effectiveness is still an active research topic. In this work, we challenge the commonly-held beliefs by showing that none of the perceived benefits is unique to normalization. Specifically, we propose fixed-update initialization (Fixup), an initialization motivated by solving the exploding and vanishing gradient problem at the beginning of training via properly rescaling a standard initialization. We find training residual networks with Fixup to be as stable as training with normalization -- even for networks with 10,000 layers. Furthermore, with proper regularization, Fixup enables residual networks without normalization to achieve state-of-the-art performance in image classification and machine translation.
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2026 4verdicts
UNVERDICTED 4representative citing papers
TaperNorm gradually removes internal normalization in pre-norm transformers via learned gates that reach zero, revealing final norm as a scale anchor and enabling up to 1.18x faster KV-cached decoding with small loss increases.
Depth expansion in normalized residual networks yields provable test-risk improvement through representational, optimization, and generalization gains under first-order descent and norm-control conditions.
A hybrid neural network and finite element strategy computes Ginzburg-Landau energy minimizers across varying kappa values, using the network output either directly or as a starting guess for reliable classical optimization.
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Gated Normalization Removal and Scale Anchoring in Pre-Norm Transformers
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