Looping Beyond Twice: A Scalable Recipe for Looped Mixture-of-Experts
Looped Transformers introduce recurrent depth as a new scaling axis for LLMs: by repeatedly applying shared AI architecture blocks, they increase effective depth without increasing parameter count. However, the benefits of looping remain unclear for large Mixture-of-Experts AI LLMs under FLOPs-matched comparisons. The main reason is that the gains from additional iterations dim
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Looped Transformers introduce recurrent depth as a new scaling axis for LLMs: by repeatedly applying shared AI architecture blocks, they increase effective depth without increasing parameter count. However, the benefits of looping remain unclear for large Mixture-of-Experts AI LLMs under FLOPs-matched comparisons. The main reason is that the gains from additional iterations dim
This research addresses a critical challenge in scaling Mixture-of-Experts (MoE) models, particularly concerning the effective use of recurrent depth. By identifying and mitigating issues like hidden-state variance, it paves the way for more efficient and performant large language models, directly impacting the cost and capability of future AI systems.