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cs.LG2026
ShellElastic: Scalable Elastic Pretraining with Simultaneous Maximal Updates and Hyperparameter Transfer
Can Jin, Yan Kang, Yifan Gong +5
Foundation models need to support diverse inference budgets, yet training a separate model for each budget duplicates pretraining effort. Elastic models train several nested sub-ne…
cs.LG2026
Complete-muE: Optimal Hyperparameter Transfer and Scaling for MoE Models
Hongwu Peng, Ohiremen Dibua, Yuanjun Xiong +8
We propose Complete-muE, a framework which targets hyperparameter transfer across dense FFN and any Mixture-of-Experts (MoE) setups in transformer blocks. Existing tools such as $μ…