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Wenyuan Xu

Wenyuan Xu has 2 papers in our library, in 2025, most of them on Language models & pretraining, Reasoning; often with Yu Yue, Yufeng Yuan, ByteDance Seed.

2 papers in our library · 2025

OpenAlex · ORCID · Wikidata · h-index 12 · 44 works · 429 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 136 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7341

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

🎓Northeast Forestry UniversityHarbin · China2024–2026 · 5 paperscurrent🎓Zhejiang UniversityHangzhou · China2018–2025 · 9 papers🏢China Shipbuilding Industry CorporationBeijing · China2016–2021 · 6 papers🎓Beijing Jiaotong UniversityBeijing · China2020–2022 · 4 papers🎓Yangzhou UniversityYangzhou · China2017–2018 · 4 papers🎓University of South CarolinaColumbia · United States2010–2012 · 2 papers🏢3MSaint Paul · United States1999–2000 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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 · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning (2025 · 62 citations)
    Seed1.5-Thinking is a reasoning model from ByteDance Seed. It works through an extended chain of thought before giving its answer. The authors identify three factors behind strong reasoning models: training data, a stable RL algorithm and scalable RL infrastructure.
  2. PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier (2025 · 8 citations)
    LLMs can write elaborate reasoning but are poor at judging whether their own answers are right. Existing fixes have drawbacks. A separate verifier model adds cost. Multi-stage RL pipelines such as SCoRe need a warm-up phase to avoid model collapse, where the second attempt barely differs from the first. PAG instead…

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. VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks ↗arXiv:2504.05118 · no summary yetYu Yue et al.2025 · 68 citations
  2. Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement LearningarXiv:2504.13914ByteDance Seed, Jiaze Chen, Tiantian Fan, et al.2025 · 62 citations
  3. PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative VerifierarXiv:2506.10406Yuhua Jiang, Yuwen Xiong, Yufeng Yuan, et al.2025 · 8 citations

 Co-authors here

8 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 136 times (of the 149,439 papers whose bibliographies we read); 46 citing papers with a page here →.

Updated

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