--- title: "Yaoyuan Wang — author profile and papers — Elamur Research" description: "Yaoyuan Wang: 3 papers on Elamur Research (2025), h-index 1 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/yaoyuan-wang/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/yaoyuan-wang/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Yaoyuan Wang # Yaoyuan Wang Yaoyuan Wang has 3 papers in our library, in 2025, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Wei Guo](https://md.research.elamur.ai/free/en/authors/wei-guo/), [Yehui Tang](https://md.research.elamur.ai/free/en/authors/yehui-tang/), [Fangcheng Liu](https://md.research.elamur.ai/free/en/authors/fangcheng-liu/). 3 papers in our library · 2025 [OpenAlex](https://openalex.org/A5003770347) · [**h-index 1**](https://openalex.org/authors/A5003770347) · [2 works](https://openalex.org/works?filter=author.id:A5003770347) · [1 citations](https://openalex.org/works?filter=author.id:A5003770347&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/yaoyuan-wang/#papers) · [🔗 Cited 17 times in our library](https://md.research.elamur.ai/free/en/authors/yaoyuan-wang/cited-by/) · [⭐ Paper Stars ranking: #10411](https://md.research.elamur.ai/free/en/paper-stars/page/105/#star-10411) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Yaoyuan+Wang) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Yaoyuan+Wang) · [dblp](https://dblp.org/search?q=Yaoyuan+Wang) · [ACL Anthology](https://aclanthology.org/search/?q=Yaoyuan+Wang) ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 6 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Vision and Pattern Recognition · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Language and Linguistics · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Hardware and Architecture · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/hardware-and-architecture/) 🧭 Topics 1 Research subjects and languages the work is about.[Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2025** 3 papers](https://md.research.elamur.ai/free/en/authors/yaoyuan-wang/year/2025/) ## Papers The most cited first (OpenAlex and our library). 1. **[Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs](https://md.research.elamur.ai/free/en/papers/tang-2025-pangu-ultra-moe-train/)** *(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. 2. **[Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs](https://md.research.elamur.ai/free/en/papers/yin-2025-pangu-ultra-pushing-limits/)** *(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. 3. **[Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity](https://md.research.elamur.ai/free/en/papers/tang-2025-pangu-pro-moe-mixture/)** *(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… ## Co-authors here 12 of 16 co-authors with a page, the most papers together first. - [Wei Guo](https://md.research.elamur.ai/free/en/authors/wei-guo/) ×3 - [Yehui Tang](https://md.research.elamur.ai/free/en/authors/yehui-tang/) ×3 - [Fangcheng Liu](https://md.research.elamur.ai/free/en/authors/fangcheng-liu/) ×2 - [Hang Zhou](https://md.research.elamur.ai/free/en/authors/hang-zhou/) ×2 - [Xiaojun Meng](https://md.research.elamur.ai/free/en/authors/xiaojun-meng/) ×2 - [Yichun Yin](https://md.research.elamur.ai/free/en/authors/yichun-yin/) ×2 - [Can Chen](https://md.research.elamur.ai/free/en/authors/can-chen/) - [Dong Li](https://md.research.elamur.ai/free/en/authors/dong-li/) - [Fei Mi](https://md.research.elamur.ai/free/en/authors/fei-mi/) - [Jinpeng Li](https://md.research.elamur.ai/free/en/authors/jinpeng-li/) - [Kai Han](https://md.research.elamur.ai/free/en/authors/kai-han/) - [Kaikai Song](https://md.research.elamur.ai/free/en/authors/kaikai-song/) ## Cited by Papers of our library cite their works 17 times (of the 149,439 papers whose bibliographies we read); [6 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/yaoyuan-wang/cited-by/). Updated 31 October 2026