AI Briefing[Paper] LatentLoop: Scaling Looped MoE Models with Asymmetric Compute Allocation
·2026.10.23 15:50
Key point
The method compresses the first loop visit and reallocates saved computation to increase hidden size across 1.3B-7B parameter models.
Details
- Proposes LatentLoop, a method for looped Mixture-of-Experts (MoE) models that uses asymmetric compute allocation to improve efficiency and performance.
- Achieves predicted compute savings of 19.0% relative to vanilla MoE and 7.9% relative to the looped baseline at a fitted NLL of 1.988.
- Consistently lowers validation NLL and improves average scores on nine benchmarks in 0-shot and 5-shot settings across 15 configurations ranging from 1.3B to 7B parameters.
- Works by compressing the first visit's sequence, retaining full resolution in the second, and reallocating saved computation to increase the hidden size.
- Shares MoE weights across visits while using separate attention parameters to accommodate different sequence resolutions.
- Largest tested budget exceeds 10^21 FLOPs.
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