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Chenglin Li

Chenglin Li has 4 papers in our library, in 2023–2026, most of them on Reasoning, Language models & pretraining, Efficient models & compression; often with Yin Zhang, Haowen Hou, Qianglong Chen.

4 papers in our library · 2023–2026

OpenAlex · h-index 3 · 12 works · 10 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 40 times in our library · ⭐ Paper Stars ranking: #7989

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 Jiao Tong UniversityShanghai · China2024–2026 · 2 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 2 works

🧭 Topics 3Research subjects and languages the work is about.Reasoning · 4 worksLanguage models & pretraining · 2 worksEfficient models & compression · 1 work

 Works by year

2020s

20262 papers20241 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…
  3. VideoPro: Adaptive Program Reasoning for Long Video Understanding (2026 · 6 citations)
    Long videos are hard for VideoLLMs because the visual cues needed to answer a question are few and scattered across a long timeline.
  4. Interleaved Latent Visual Reasoning with Selective Perceptual Modeling (2026 · 5 citations)
    Multimodal LLMs can reason better when they interleave intermediate images with their textual chain of thought.

 Co-authors here

12 of 16 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 40 times (of the 149,439 papers whose bibliographies we read); 14 citing papers with a page here →.

Updated

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