New Research: Generative Models Can Insert Tokens Anywhere
thjashin · x · 2026-07-06
Traditional autoregressive (AR) models are limited in expressiveness as they can only append tokens to the end of a sequence. This paper introduces "insertion-based generative models" that allow token insertion at arbitrary positions, offering stronger theoretical expressiveness but posing challenges for standard maximum likelihood training.
To address this, the research introduces a scalable variational learning method, providing a novel approach for generative models looking to move beyond the AR paradigm.
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