---
title: "Yunhua Zhou — author profile and papers — Elamur Research"
description: "Yunhua Zhou, Shanghai Artificial Intelligence Laboratory: 6 papers on Elamur Research (2024–2026), h-index 11 — affiliations, topics…"
canonical: "https://research.elamur.ai/free/en/authors/yunhua-zhou/"
---
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# Yunhua Zhou

Yunhua Zhou has 6 papers in our library, in 2024–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/), [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/); often with [Hang Yan](https://md.research.elamur.ai/free/en/authors/hang-yan/), [Linyang Li](https://md.research.elamur.ai/free/en/authors/linyang-li/), [Qipeng Guo](https://md.research.elamur.ai/free/en/authors/qipeng-guo/).

6 papers in our library · 2024–2026

[OpenAlex](https://openalex.org/A5102710198) · [ORCID](https://orcid.org/0000-0002-6907-7167) · [**h-index 11**](https://openalex.org/authors/A5102710198) · [84 works](https://openalex.org/works?filter=author.id:A5102710198) · [501 citations](https://openalex.org/works?filter=author.id:A5102710198&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 6 works in our library](https://md.research.elamur.ai/free/en/authors/yunhua-zhou/#papers) · [🔗 Cited 193 times in our library](https://md.research.elamur.ai/free/en/authors/yunhua-zhou/cited-by/) · [3 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #6280](https://md.research.elamur.ai/free/en/paper-stars/page/63/#star-6280)

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=Yunhua+Zhou) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Yunhua+Zhou) · [dblp](https://dblp.org/search?q=Yunhua+Zhou) · [ACL Anthology](https://aclanthology.org/search/?q=Yunhua+Zhou)

## Affiliations

[🔬**Shanghai Artificial Intelligence Laboratory** Shanghai · China 2024–2026 · 12 papers*current*](https://md.research.elamur.ai/free/en/labs/shanghai-artificial-intelligence-laboratory/) · [🎓**Chongqing University** Chongqing · China 2004–2025 · 25 papers](https://md.research.elamur.ai/free/en/universities/chongqing-university/) · [🎓**Fudan University** Shanghai · China 2022–2024 · 11 papers](https://md.research.elamur.ai/free/en/universities/fudan-university/) · [🎓**Nanjing University of Posts and Telecommunications** Nanjing · China 2017–2018 · 7 papers](https://ror.org/043bpky34) · [🎓**Peking University** Beijing · China 2007–2008 · 2 papers](https://md.research.elamur.ai/free/en/universities/peking-university/)

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 5 The field of science this work belongs to (OpenAlex fields).[Computer Science · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Arts and Humanities · 5 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Artificial Intelligence · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 5 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/)

🧭 Topics 4 Research subjects and languages the work is about.[Language models & pretraining · 4 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Vision & language · 2 works](https://md.research.elamur.ai/free/en/topics/multimodal/) · [Evaluation & benchmarks · 1 work](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/)

## Works by year

### 2020s

[**2026** 3 papers](https://md.research.elamur.ai/free/en/authors/yunhua-zhou/year/2026/) · [**2025** 1 paper](https://md.research.elamur.ai/free/en/authors/yunhua-zhou/year/2025/) · [**2024** 2 papers](https://md.research.elamur.ai/free/en/authors/yunhua-zhou/year/2024/)

## Papers

The most cited first (OpenAlex and our library).

1. **[AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling](https://md.research.elamur.ai/free/en/papers/zhan-2024-anygpt-unified-multimodal-llm-discrete/)** *(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?](https://md.research.elamur.ai/free/en/papers/zeng-2025-revisiting-test-time-scaling-o1/)** *(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](https://md.research.elamur.ai/free/en/papers/yang-2026-which-reasoning-trajectories-teach-students/)** *(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](https://md.research.elamur.ai/free/en/papers/zou-2026-intern-s1-pro-scientific/)** *(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?](https://md.research.elamur.ai/free/en/papers/zhou-2026-how-set-learning-rate-large/)** *(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?](https://md.research.elamur.ai/free/en/papers/lei-2024-gaokao-eval-high-scores/)** *(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 Modeling](https://md.research.elamur.ai/free/en/papers/zhan-2024-anygpt-unified-multimodal-llm-discrete/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jun Zhan, Junqi Dai, Jiasheng Ye, et al.*2024 · 95 citations*
2. **[KNN-Contrastive Learning for Out-of-Domain Intent Classification ↗](https://aclanthology.org/2022.acl-long.352/) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yet**Yunhua Zhou, Peiju Liu, Xipeng Qiu*2022 · 85 citations*
3. **[Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance ↗](https://arxiv.org/abs/2403.16952) arXiv:2403.16952 · no summary yet**Jiasheng Ye et al.*2024 · 52 citations*
4. **[Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?](https://md.research.elamur.ai/free/en/papers/zeng-2025-revisiting-test-time-scaling-o1/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 ↗](https://arxiv.org/abs/2412.14135) arXiv:2412.14135 · no summary yet**Zhiyuan Zeng et al.*2024 · 33 citations*
6. **[Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment](https://md.research.elamur.ai/free/en/papers/yang-2026-which-reasoning-trajectories-teach-students/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuming Yang, Mingyoung Lai, Wanxu Zhao, et al.*2026 · 4 citations*
7. [**Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale arXiv:2603.25040**Yicheng Zou, Dongsheng Zhu, Lin Zhu, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/zou-2026-intern-s1-pro-scientific/)
8. **[How to Set the Learning Rate for Large-Scale Pre-training?](https://md.research.elamur.ai/free/en/papers/zhou-2026-how-set-learning-rate-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yunhua Zhou, Shuhao Xing, Junhao Huang, et al.*2026 · 1 citation*
9. [**GAOKAO-Eval: Does high scores truly reflect strong capabilities in LLMs? arXiv:2412.10056**Zhikai Lei, Tianyi Liang, Hanglei Hu, et al.*2024 · 1 citation*](https://md.research.elamur.ai/free/en/papers/lei-2024-gaokao-eval-high-scores/)

## Co-authors here

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

- [Hang Yan](https://md.research.elamur.ai/free/en/authors/hang-yan/) ×2
- [Linyang Li](https://md.research.elamur.ai/free/en/authors/linyang-li/) ×2
- [Qipeng Guo](https://md.research.elamur.ai/free/en/authors/qipeng-guo/) ×2
- [Xipeng Qiu](https://md.research.elamur.ai/free/en/authors/xipeng-qiu/) ×2
- [Yicheng Zou](https://md.research.elamur.ai/free/en/authors/yicheng-zou/) ×2
- [Bowen Zhou](https://md.research.elamur.ai/free/en/authors/bowen-zhou/)
- [Dong Zhang](https://md.research.elamur.ai/free/en/authors/dong-zhang/)
- [Ge Zhang](https://md.research.elamur.ai/free/en/authors/ge-zhang/)
- [Haijun Lv](https://md.research.elamur.ai/free/en/authors/haijun-lv/)
- [Jiasheng Ye](https://md.research.elamur.ai/free/en/authors/jiasheng-ye/)
- [Jie Fu](https://md.research.elamur.ai/free/en/authors/jie-fu/)
- [Jin Zhang](https://md.research.elamur.ai/free/en/authors/jin-zhang/)

## Venues

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

- [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×4: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/), [ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/), [ACL 2024](https://md.research.elamur.ai/free/en/venues/acl/2024/)
- [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)

## 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 →](https://md.research.elamur.ai/free/en/authors/yunhua-zhou/cited-by/).

Updated 31 October 2026