arXiv:2605.26444v1 Announce Type: new Abstract: Large language models typically employ vocabularies of over 100k tokens, which creates a major computational bottleneck at the final linear projection layer when performing speculative decoding. Current methods for vocabulary pruning depend on either fixed or coarse-grained sub-vocabularies, requiring around 30k active tokens to preserve the quality of the draft model. We introduce MicroSpec, a training-free technique that overcomes this limitation by building a compact, context-sensitive active vocabulary on the fly for every decoding step. Expl

Source: arXiv cs.CL — read the full report at the original publisher.

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