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Yuanshun Yao

Yuanshun Yao has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning, Bias, fairness & safety; often with Jingyu Zhang, Madian Khabsa, Shengjie Bi.

2 papers in our library · 2023–2026

OpenAlex · h-index 13 · 38 works · 2,546 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 129 times in our library · ⭐ Paper Stars ranking: #5933

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

🏢ByteDanceBeijing · China2023–2025 · 10 papers🎓University of ChicagoChicago · United States2017–2023 · 8 papers🎓University of California, Santa BarbaraSanta Barbara · United States2017 · 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).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Bias, fairness & safety · 1 workLanguage models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Large Language Model Unlearning (2023 · 174 citations)
    The paper treats unlearning as a cheap alignment tool for LLMs. The idea is to make a model drop unwanted behaviour using only negative examples, such as harmful replies found through red teaming or user reports, without the human-written positive answers that RLHF needs. The authors define the setting, goals and…
  2. Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models (2026)
    Reinforcement learning with verifiable rewards (RLVR) improves math and multimodal reasoning, but it tends to wear away skills a model already had, such as perception, OCR and robustness.

 Co-authors here

5 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 129 times (of the 149,439 papers whose bibliographies we read); 25 citing papers with a page here →.

Updated

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