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About this paper
Zefeng He, Yunkai Chai, Xiaoyang Chen, Tong Zhu, Zongliang Wu, Yun Luo, Yafu Li, Yu Cheng
LatentLoop reallocates computation in looped MoE models by compressing the sequence for the first visit, writing its update back to full resolution, and processing the full sequence on the second visit. Across 15 matched configurations, it has lower validation negative log-likelihood than baselines. Fitted curves predict 19.0% and 7.9% less training computation at equal loss, within the observed allocation range.
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