Paper recorded by Signals 4 on 2026-09-28 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-28 on arXiv · recorded by Signals 4 on 2026-09-29
Category: cs.AI · 人工智能 · first seen 2026-09-29
One deployed language model must often serve many compute budgets, yet serving each budget still means a separate training or compression run per point. We train a Telescopic Language Model (TLM) to be that continuum: a nested-capacity Transformer supervised by stochastic prefix supervision with a full anchor. At every step, one randomly truncated prefix of the capacity axis is trained against the