Hayden Prairie, Jacob M. Ryan, Taylor Berg-Kirkpatrick, Daniel Y. Fu · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31633 · open-access PDF
循环架构在保持参数计数时保持尺度质量,重复通过相同层块的激活。在推断过程中,每个重复产生独立的KV缓存,导致记忆线性增长。为了应对这一挑战,先前的工作提出了KV共享(即深度共享KV缓存),发现,在某些情况下,最终重复的KV缓存可以与以前的重复共享,而无需质量损失,这是我们称之为KV共享的属性。
Machine translation (Meta M2M100); the English original follows.
Looped architectures scale quality while keeping parameter count fixed by repeatedly passing activations through the same block of layers. During inference, each recurrence generates independent KV-caches, causing memory to grow linearly. To address this challenge, prior work has proposed KV-sharing (i.e., sharing KV-caches across depth), finding that, in some cases, the KV-cache of the final recurrence can be shared with the earlier recurrences without quality loss, a property we denote as KV-shareability. However, the mechanisms underlying KV-shareability remain poorly understood, with even
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