STAPLE Adds Sparse Embedding Modules to Pretrained Checkpoints
Sparse upcycling usually means expensive retraining from scratch, so a method that grafts new capacity onto an existing checkpoint could cut the cost of scaling older models. The nuance matters: this is a lightweight FFN modification, not a full Engram architecture, so results depend on how much quality the added token-indexed embeddings recover. Researchers and engineers fine-tuning open-source checkpoints should follow the paper for the training recipe.
