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Yunhua Zhou

Yunhua Zhou has 6 papers in our library, in 2024–2026, most of them on Language models & pretraining, Vision & language, Evaluation & benchmarks; often with Hang Yan, Linyang Li, Qipeng Guo.

6 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 11 · 84 works · 501 citations OpenAlex, 2026-10-14

📄 6 works in our library · 🔗 Cited 193 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #6280

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

🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2024–2026 · 12 paperscurrent🎓Chongqing UniversityChongqing · China2004–2025 · 25 papers🎓Fudan UniversityShanghai · China2022–2024 · 11 papers🎓Nanjing University of Posts and TelecommunicationsNanjing · China2017–2018 · 7 papers🎓Peking UniversityBeijing · China2007–2008 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 7 more institutions on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 6 worksArts and Humanities · 5 worksArtificial Intelligence · 6 worksLanguage and Linguistics · 5 worksComputer Vision and Pattern Recognition · 3 works

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 4 worksVision & language · 2 worksEvaluation & benchmarks · 1 workReasoning · 1 work

 Works by year

2020s

20263 papers20251 paper20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling (2024 · 95 citations)
    AnyGPT addresses the challenge of building one language model that can both understand and generate several non-text modalities (speech, images, music) in any combination.
  2. Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities? (2025 · 38 citations)
    Open o1-style reasoning models such as QwQ, Deepseek-R1 (R1) and LIMO generate very long chains of thought (CoT).
  3. Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment (2026 · 4 citations)
    Distilling reasoning from a teacher LLM into a student through SFT on long chain-of-thought (CoT) traces does not reliably improve with teacher strength.
  4. Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale (2026 · 3 citations)
    Intern-S1-Pro, from Shanghai AI Laboratory, is presented as the first scientific multimodal foundation model with one trillion parameters.
  5. How to Set the Learning Rate for Large-Scale Pre-training? (2026 · 1 citation)
    Choosing the learning rate (LR) for large-scale pre-training is expensive to get wrong and too costly to tune by trial at full scale.
  6. GAOKAO-Eval: Does high scores truly reflect strong capabilities in LLMs? (2024 · 1 citation)
    LLMs often score well on human-designed benchmarks, but it is unclear whether those scores reflect real, human-like ability or are inflated by data leakage and narrow test coverage.

All works

9 works of theirs in our library, the most cited first; 6 with a page here, the others link their original until we summarise them.

  1. AnyGPT: Unified Multimodal LLM with Discrete Sequence ModelingAnnual Meeting of the Association for Computational Linguistics (ACL)Jun Zhan, Junqi Dai, Jiasheng Ye, et al.2024 · 95 citations
  2. KNN-Contrastive Learning for Out-of-Domain Intent Classification ↗Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetYunhua Zhou, Peiju Liu, Xipeng Qiu2022 · 85 citations
  3. Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance ↗arXiv:2403.16952 · no summary yetJiasheng Ye et al.2024 · 52 citations
  4. Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?Annual Meeting of the Association for Computational Linguistics (ACL)Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin, et al.2025 · 38 citations
  5. Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective ↗arXiv:2412.14135 · no summary yetZhiyuan Zeng et al.2024 · 33 citations
  6. Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative AlignmentAnnual Meeting of the Association for Computational Linguistics (ACL)Yuming Yang, Mingyoung Lai, Wanxu Zhao, et al.2026 · 4 citations
  7. Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion ScalearXiv:2603.25040Yicheng Zou, Dongsheng Zhu, Lin Zhu, et al.2026 · 3 citations
  8. How to Set the Learning Rate for Large-Scale Pre-training?Findings of the Association for Computational LinguisticsYunhua Zhou, Shuhao Xing, Junhao Huang, et al.2026 · 1 citation
  9. GAOKAO-Eval: Does high scores truly reflect strong capabilities in LLMs?arXiv:2412.10056Zhikai Lei, Tianyi Liang, Hanglei Hu, et al.2024 · 1 citation

 Co-authors here

12 of 31 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 193 times (of the 149,439 papers whose bibliographies we read); 44 citing papers with a page here →.

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