Attiano Purpura-Pontoniere

STEM: SCALING TRANSFORMERS WITH EMBEDDING MODULES

Fine-grained sparsity promises higher parametric capacity without proportional per-token compute, but often suffers from training instability, load balancing, and communication overhead. We introduce \textbf{STEM} (\emph{Scaling Transformers with Embedding Modules}), a static, token-indexed approach…