Let's Scale Step by Step: Compute-Efficient Hyperparameter Transfer for Large-Scale Mixture-of-Experts
Score
77首次将μP系统扩展到MLA+Muon的MoE架构并联合token维度外推，填补了大规模MoE超参数迁移的方法空白。
理论推导与实证验证结合严密，155B模型预训练进一步增强了结论的可信度。
为工业界大模型训练提供了可直接落地的低算力调参方案，能显著降低计算成本与试验周期。
当前MoE是大模型主流架构，高计算效率调参方法紧密契合前沿需求。
来源为arXiv论文预印本，尚未经同行评审，但方法验证完整且作者做过大规模实测。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
The method is highly reproducible, directly addresses the expensive tuning pain point in large-scale MoE training, and includes full industrial-scale validation.
AI commentary
「The most valuable takeaway is turning hyperparameter transfer from an empirical trick into a quantifiable extrapolation law, especially the token-dimension prediction at R²=0.95, which is highly relevant for compute planning. For researchers training MoE or preparing ultra-large-scale pretraining, this could be a key tool to save massive trial-and-error costs.」
Quick overview
Mixture-of-Experts (MoE) architectures expand model capacity at sublinear computational cost, yet sweeping hyperparameters—especially learning rate—at extreme model sizes and trillion-token budgets remains prohibitive. This paper proposes a compute-efficient two-step transfer framework: first, adapt Maximal Update Parameterization (μP) to MoE models with Multi-head Latent Attention (MLA) and the Muon optimizer, showing that optimal learning rates transfer consistently across width-scaled models; second, extend transferability along the token dimension via a predictive scaling law. Linear regression on small proxy runs extrapolates the ideal learning rate to massive horizons (e.g., 10 trillion tokens) with high fidelity (R²=0.95). The authors pretrain a 155B-total / 17B-active foundation model from scratch, validating that full-scale optimal configurations can be predicted with minimal ablation cost.
- Proposes a μP adaptation for MoE architectures with MLA and the Muon optimizer, demonstrating width-wise transfer of optimal learning rates.
- Establishes a token-dimension predictive scaling law, extrapolating learning rates to 10-trillion-token horizons with R²=0.95.
- Validates on a 155B-total/17B-active foundation model pretrained from scratch, showing small proxy runs can predict full-scale optimal configurations.
Full text
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