Where Should Optimizer State Live? Tiered State Allocation for Memory-Efficient Mixture-of-Experts Training
Score
83提出优化器状态位置与数量同等重要的新视角，对MoE内存优化提供了独特解决方案。
实验设计完整，包含消融、基线和超参数调优，结论可靠。
直接降低显存需求97%以上，使得MoE训练可在单卡40GB加速器上运行，极具工程价值。
论文为最新提交（arXiv 2607），紧贴当前大规模模型训练内存瓶颈的热点。
arXiv预印本，虽未经同行评审，但方法清晰、实验可复现，作者机构未知。
Base 92 × T2 Source weight ×0.9 = 83
Curation line 70Selected
Why selected
The paper presents an innovative and effective solution for memory optimization in MoE training, with rigorous experimental design, comprehensive comparisons, and significant practical value.
AI commentary
「This paper is highly valuable for teams training MoE models: it demonstrates that with careful optimizer state allocation, one can dramatically reduce memory without sacrificing accuracy—and even improve performance. It is especially relevant for labs or enterprises constrained by 40 GB or 80 GB GPU memory. The tiered strategy and ablation on momentum retention are worth studying for optimizing training frameworks like DeepSpeed or Megatron.」
Quick overview
SkewAdam assigns different optimizer state formats to the backbone, experts, and router of MoE, compressing AdamW's 50.6 GB state to 1.29 GB (2.6%), reducing peak training memory from 81.4 GB to 31.3 GB while achieving better validation perplexity (108.4 vs AdamW 126.8, Muon 120.2, Lion 393.7). Results show that where state lives matters as much as how much there is; retaining momentum is critical for accuracy, while tiered allocation saves memory without harming performance.
- Optimizer state is the largest memory consumer in MoE training, occupying over 80% of AdamW's memory budget.
- SkewAdam uses float32 momentum plus factored second moment for the backbone (5% params), only factored second moment for experts (95%), and exact second moment for the router (<0.01%).
- Compared to AdamW, state memory reduces by 97.4%, peak memory by 61.5%, with improved perplexity.
- Momentum is critical for accuracy; merely reducing state (e.g., Adafactor) causes significant performance degradation.
Full text
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