STAPLE paper shows you can cheaply sparse-upcycle pretrained checkpoints with embeddings
teortaxesTex · x · 2026-10-20
X user teortaxesTex highlights the paper STAPLE: Static Sparse Upcycling of Pretrained Checkpoints via Embedding Modules, concluding that you can in fact Engram-augment a pretrained checkpoint.
Key points:
- Not quite identical to the original approach: STAPLE adds a token-indexed embedding to the up-projection inside the existing FFN
- Positioned as a cheaper, faster sparse upcycling route to extend pretrained weights without retraining from scratch
Related event: STAPLE: Sparse Memory Upcycling for Pretrained Models(2 posts)→
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