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Yali Du

Yali Du has 2 papers in our library, in 2025, most of them on Reasoning, Language models & pretraining; often with Yulan He, Chen Qian, Hanqi Yan.

2 papers in our library · 2025

OpenAlex · ORCID · h-index 15 · 75 works · 1,288 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 93 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7067

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

🎓King's College LondonLondon · United Kingdom2021–2026 · 114 paperscurrent🔬The Alan Turing InstituteLondon · United Kingdom2025–2026 · 11 paperscurrent🎓Shanghai Jiao Tong UniversityShanghai · China2025 · 1 papercurrent🎓University College LondonLondon · United Kingdom2021–2026 · 14 papers🎓University of Technology SydneySydney · Australia2017–2019 · 9 papers🔬Shenzhen Institutes of Advanced TechnologyShenzhen · China2018 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

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

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation (2025 · 78 citations)
    The paper asks whether a language model can reason in hidden vector space rather than writing out a chain-of-thought (CoT) and still match explicit CoT accuracy.
  2. NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning (2025 · 7 citations)
    Incentive training rewards a model only on its final answer, which pushes it to produce intermediate reasoning on its own.

All works

3 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. CODI: Compressing Chain-of-Thought into Continuous Space via Self-DistillationConference on Empirical Methods in Natural Language Processing (EMNLP)Zhenyi Shen, Hanqi Yan, Linhai Zhang, et al.2025 · 78 citations
  2. ChessGPT: Bridging Policy Learning and Language Modeling ↗arXiv:2306.09200 · no summary yetXidong Feng et al.2023 · 34 citations
  3. NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement LearningConference on Empirical Methods in Natural Language Processing (EMNLP)Wei Liu, Siya Qi, Xinyu Wang, et al.2025 · 7 citations

 Co-authors here

6 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 93 times (of the 149,439 papers whose bibliographies we read); 27 citing papers with a page here →.

Updated

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