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Zhongwei Wan

Zhongwei Wan has 2 papers in our library, in 2024–2025, most of them on Reasoning, Efficient models & compression; often with Chaofan Tao, Che Liu, Daniel Rueckert.

2 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 9 · 73 works · 303 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 140 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #7666

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

🎓The Ohio State UniversityColumbus · United States2023–2026 · 46 paperscurrent🏢Alibaba GroupHangzhou · China2026 · 1 papercurrent🎓University of Chinese Academy of SciencesBeijing · China2022–2023 · 3 papers🏛️Chinese Academy of SciencesBeijing · China2022 · 2 papers🔬Shenzhen Institutes of Advanced TechnologyShenzhen · China2022 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 worksMedicine · 1 workHealth Informatics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Efficient models & compression · 1 workReasoning · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Scaling laws · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies (2024 · 46 citations)
    Standard scaling laws for LLMs predict loss from parameter count and training data but treat vocabulary size as fixed.
  2. Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL (2025 · 10 citations)
    Medical LLMs usually learn to reason through supervised fine-tuning on chain-of-thought (CoT) traces, often distilled from proprietary systems such as GPT-4o.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Efficient Large Language Models: A Survey ↗arXiv:2312.03863 · no summary yetZhongwei Wan et al.2023 · 110 citations
  2. Scaling Laws with Vocabulary: Larger Models Deserve Larger VocabulariesarXiv:2407.13623Chaofan Tao, Qian Liu, Longxu Dou, et al.2024 · 46 citations
  3. Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RLarXiv:2505.17952Che Liu, Haozhe Wang, Jiazhen Pan, et al.2025 · 10 citations
  4. DGLF: A Dual Graph-based Learning Framework for Multi-modal Sarcasm Detection ↗Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · no summary yetZhihong Zhu et al.2024 · 9 citations

 Co-authors here

12 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 140 times (of the 149,439 papers whose bibliographies we read); 28 citing papers with a page here →.

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