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LatentLoop reallocates compute across visits in looped MoE models

huggingface.co by Zefeng He et al. Editor score 8/10

LatentLoop compresses the first loop visit, keeps full resolution for the second, and reallocates saved compute to hidden size; it lowers validation NLL across 15 configurations from 1.3B to 7B parameters. The results offer a concrete architecture trade-off for compute-constrained model training.

What
LatentLoop is a looped Mixture-of-Experts model that allocates computation asymmetrically across two visits to a shared block.
Method
It compresses the sequence on the first visit, keeps full resolution on the second, and reallocates saved computation to increase hidden size.
Why it matters
The approach lowers validation NLL across 15 configurations, offering engineers a way to use compute more effectively in looped MoE models.
applies compressed se… applies shared weights shared weights separate parameters separate parameters first visit shared block MoE weights attentionparameters second visit
LatentLoop uses shared MoE weights across visits with separate attention parameters. Sketched from the article's own text.

Read on huggingface.co → Code · 1 stars

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