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Speculative decoding drafters no longer need to be trained from scratch per model: target-agnostic pretraining on pruned small LMs produces a single, reusable backbone that outperforms bespoke drafters by up to 22.7% across completely different target architectures.
Forget SVD: CARE aligns low-rank attention approximations with input activations, boosting accuracy up to 1.7x and slashing perplexity by 215x when converting models to multi-head latent attention.
