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Yahui Zhou

Yahui Zhou has 2 papers in our library, in 2024, most of them on Language models & pretraining, Reasoning; often with Jujie He, Liang Zeng, Shuicheng Yan.

2 papers in our library · 2024

current affiliationJiangnan University · Wuxi China; Shanghai Jiao Tong University · Shanghai China; Shanghai Chest Hospital · Shanghai China; China University of Petroleum, East China · Qingdao China; Hubei University · Wuhan China

OpenAlex · ORCID · Wikidata · h-index 17 · 54 works · 883 citations OpenAlex, 2026-10-14

2 works in our library · 321 citations in our library · 5 more works in our library · ⭐ Paper Star rank #4082

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

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

Affiliations

🎓Jiangnan UniversityWuxi · China2023–2026 · 9 paperscurrent🎓Hubei UniversityWuhan · China2026 · 3 paperscurrent🎓China University of Petroleum, East ChinaQingdao · China2026 · 2 paperscurrent🏥Shanghai Chest HospitalShanghai · China2025–2026 · 2 paperscurrent🎓Shanghai Jiao Tong UniversityShanghai · China2025–2026 · 2 paperscurrent🎓Nanjing Medical UniversityNanjing · China2016–2022 · 20 papers🏥Nanjing Maternity and Child Health Care HospitalNanjing · China2016–2022 · 18 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 9 more institutions on single bylines are not listed.

Topics

 Language models & pretraining2 Reasoning1

Works by year

2020s

20242 papers

Papers

The most cited first (OpenAlex and our library).

  1. Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs (2024 · 104 citations)
    Reward models turn human preferences into a training signal for LLM alignment. Their quality depends heavily on the preference data, and open-source preference datasets tend to be noisy or inconsistently labelled. The authors take a data-first approach. Using only public sources (HelpSteer2, OffsetBias, WildGuardMix…
  2. Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models — The Story Goes On (2024 · 14 citations)
    The paper asks whether strong mathematical reasoning really requires very large models, and whether supervised fine-tuning (SFT) only changes output style.

Also in our library

5 more works of theirs are in our library without a page here yet; they will appear as we publish them.

Co-authors here

11 co-authors with a page, the most papers together first.

Cited by

Papers of our library cite their works 321 times (of the 140,884 papers whose bibliographies we read); 15 citing papers with a page here →.

This page in Markdown → https://md.research.elamur.ai/free/en/authors/yahui-zhou/