Token-Gated Language Models Claim Up to 11x Faster Prefill by Skipping Tokens

Liana Patel introduces Token-Gated Language Models, which learn to skim context and forward only the tokens that matter, according to a post by Matei Zaharia.

Zaharia describes TokenGate as a trainable module that works with existing models to speed up long-context inference. The up-to-11x prefill speedup is the figure Patel's introduction gives; the reports do not include benchmark details, models tested, or the accuracy impact of skipping tokens.