Yaoyuan Wang

Yaoyuan Wang has 3 papers in our library, in 2025, most of them on Language models & pretraining; often with Wei Guo, Yehui Tang, Fangcheng Liu.

3 papers in our library · 2025

The profile is the library's match of the name; two people with one name are not always told apart.

Fields, topics and concepts

Of its works in our library, with the number of works on each.

The most cited first (OpenAlex and our library).

Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs (2025 · 8 citations)
Training MoE language models with nearly a trillion parameters is hard: the hardware is underused because the set of experts each token activates changes at runtime, and the scale strains communication and memory.
Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs (2025 · 6 citations)
Pangu Ultra is Huawei's effort to show that a very deep dense Transformer, trained entirely on Ascend NPUs instead of GPUs, can compete with much larger sparse MoE systems.
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity (2025 · 4 citations)
Sparse Mixture-of-Experts LLMs are cheap per token. However, when experts are spread across accelerators, uneven routing overloads some devices, and the slowest device limits overall speed. The Pangu Team at Huawei proposes Mixture of Grouped Experts (MoGE). Experts are split into equal, non-overlapping groups…

12 of 16 co-authors with a page, the most papers together first.

Papers of our library cite their works 17 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated 31 October 2026