A Unified View of Attention and Residual Sinks: Outlier-Driven Rescaling is Essential for Transformer Training
Paper • 2601.22966 • Published
NN Arch Components
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STEM: Scaling Transformers with Embedding Modules
Paper • 2601.10639 • Published • 2 -
Deep Delta Learning
Paper • 2601.00417 • Published • 34 -
mHC: Manifold-Constrained Hyper-Connections
Paper • 2512.24880 • Published • 322 -
VersatileFFN: Achieving Parameter Efficiency in LLMs via Adaptive Wide-and-Deep Reuse
Paper • 2512.14531 • Published • 15 -
Stronger Normalization-Free Transformers
Paper • 2512.10938 • Published • 22 -
Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free
Paper • 2505.06708 • Published • 11 -
Transformers without Normalization
Paper • 2503.10622 • Published • 172 -
Forgetting Transformer: Softmax Attention with a Forget Gate
Paper • 2503.02130 • Published • 32 -
Hyper-Connections
Paper • 2409.19606 • Published • 26 -
Virtual Width Networks
Paper • 2511.11238 • Published • 39