New Gated Recurrent Transformer Cuts Params and Memory by 60%
burkov · x · 2026-08-30
A paper from Cerebras, GUC, and TUM introduces a gated recurrent transformer architecture. It uses state-conditioned modulation across iterated shared layers to match standard language model performance while reducing parameters and peak decoding memory by roughly sixty percent.
Related event: Gated Recurrent Transformer Matches GPT-2 with Far Fewer Parameters(2 posts)→
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