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cs.CL2026
Variable-Width Transformers
Zhaofeng Wu, Oliver Sieberling, Shawn Tan +3
Scaling model size, specifically depth and width, has driven significant progress in transformer-based language models. However, most architectures maintain a constant width across…
cs.CL2025
Distilling to Hybrid Attention Models via KL-Guided Layer Selection
Yanhong Li, Songlin Yang, Shawn Tan +4
Distilling pretrained softmax attention Transformers into more efficient hybrid architectures that interleave softmax and linear attention layers is a promising approach for improv…