TokenGate teaches LLMs to skim context, achieving up to 11.3x faster prefill
berkeley_ai · x · 2026-10-24
- Ilya Sutskever (@istoica05) amplified TokenGate (Token-Gated Language Models), a new efficiency research project.
- The idea: train LLMs to conditionally skim context tokens, forwarding only the tokens that matter through the full computation instead of processing everything.
- Results: up to 11.3x faster prefill and drastically reduced peak memory for long-context tasks.
- The authors frame it as analogous to the dense-to-MoE shift: the next frontier of efficient LLMs is conditional computation along a new scaling axis — context tokens.
Related event: TokenGate teaches LLMs to skim context, speeding prefill up to 11.3x(2 posts)→
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