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Shuntian Yao

Shuntian Yao has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining; often with Hanyu Lai, Xiao Liu, Dan Zhang.

2 papers in our library · 2024–2025

OpenAlex · ORCID · Wikidata · h-index 9 · 28 works · 476 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 58 times in our library · ⭐ Paper Stars ranking: #6640

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

🎓Beijing University of Posts and TelecommunicationsBeijing · China2024–2025 · 3 paperscurrent🎓Tsinghua UniversityBeijing · China2024–2025 · 3 paperscurrent🎓Nanyang Technological UniversitySingapore · Singapore2002–2012 · 14 papers🎓Yale UniversityNew Haven · United States1989–1990 · 2 papers

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).Computer Science · 2 worksInformation Systems · 1 workArtificial Intelligence · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

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

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. AutoWebGLM: A Large Language Model-based Web Navigating Agent (2024 · 96 citations)
    Web agents built on LLMs often fail on real websites. Pages are long and noisy, the set of possible browser actions is broad, and open-web tasks are hard to plan. Existing agents also struggle to recover from their own mistakes. The authors address this with AutoWebGLM, an open agent fine-tuned from ChatGLM3-6B. It…
  2. A Survey of Post-Training Scaling in Large Language Models (2025 · 18 citations)
    Pre-training scaling, which ties loss to parameter count and token volume, is reaching its limits. High-quality web text is running out, synthetic corpora have unproven value at scale, and the returns on ever-larger compute budgets look doubtful. This survey, by authors from Tsinghua University and Zhipu AI, argues…

 Co-authors here

12 of 15 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 58 times (of the 149,439 papers whose bibliographies we read); 19 citing papers with a page here →.

Updated

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