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Yicheng Li

Yicheng Li has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining, Reasoning, Efficient models & compression; often with Chenglin Li, Yin Zhang, Fei Yu.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 12 · 58 works · 600 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 29 times in our library · ⭐ Paper Stars ranking: #8405

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

🎓Jiangsu UniversityZhenjiang · China2019–2026 · 31 paperscurrent🎓Zhejiang UniversityHangzhou · China2016–2024 · 6 papers🎓Tongji UniversityShanghai · China2017–2023 · 4 papers🎓North Minzu UniversityYinchuan · China2024–2025 · 3 papers🎓Jilin UniversityChangchun · China2024 · 3 papers🎓East China University of Science and TechnologyShanghai · China2025 · 2 papers🔬Guangxi Key Laboratory of Clean Pulp and Papermaking and Pollution ControlNanning · China2025 · 2 papers🎓Guangxi UniversityNanning · China2025 · 2 papers🔬State Key Laboratory of Modern Optical InstrumentsHangzhou · China2016–2019 · 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 2 worksEfficient models & compression · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Teaching Small Language Models Reasoning through Counterfactual Distillation (2024 · 21 citations)
    The paper addresses a weakness of chain-of-thought (CoT) distillation, where a large teacher model's rationales are used to fine-tune small language models (SLMs).
  2. Mixed Distillation Helps Smaller Language Model Better Reasoning (2023 · 11 citations)
    Large language models reason well but are expensive to run. Earlier work on distilling reasoning into compact models mostly transfers only natural-language Chain of Thought (CoT). This paper argues that Program of Thought (PoT), where the model writes executable Python, is a valuable extra training signal. The…

 Co-authors here

8 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 29 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated

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