SMELT: Scaling Laws for Compute-Matched MoE Looped Transformers
ByteDance-Seed · hf · 2026-09-02
ByteDance presents SMELT, investigating how looping middle layers in sparse Mixture-of-Experts Transformers improves training efficiency and downstream performance. This method optimizes architecture while matching per-token FLOPs, parameters, and cache budgets.
Related event: ByteDance's SMELT: Looping MoE Layers Cuts Training Compute Up to 18%(4 posts)→
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