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Chenjun Xiao

Chenjun Xiao has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining; often with Angang Du, Bofei Gao, Bowei Xing.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 7 · 43 works · 204 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 944 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4175

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

🎓Chinese University of Hong Kong, ShenzhenShenzhen · China2023–2026 · 7 papersEmploymentcurrent🎓University of AlbertaEdmonton · Canada2016–2023 · 16 papers🏢Huawei TechnologiesShenzhen · China2023–2024 · 3 papers🏢GoogleMountain View · United States2020–2021 · 3 papers🎓Northeastern UniversityShenyang · China2012–2015 · 3 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 9The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksArts and Humanities · 1 workComputer Vision and Pattern Recognition · 1 workLanguage and Linguistics · 1 workMathematics · 1 workDecision Sciences · 1 workInformation Systems · 1 workManagement Science and Operations Research · 1 work

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

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Kimi k1.5: Scaling Reinforcement Learning with LLMs (2025 · 431 citations)
    Next-token pretraining is limited by how much high-quality text exists. The Kimi Team proposes reinforcement learning as another way to keep scaling: the model creates its own training signal by exploring solutions and receiving rewards. The report describes how they built Kimi k1.5, a multimodal LLM. Training runs…
  2. Large Language Model-Enhanced Multi-Armed Bandits (2026 · 1 citation)
    Earlier work prompts a pretrained LLM to pick bandit arms directly, but these agents often explore poorly.

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. Kimi k1.5: Scaling Reinforcement Learning with LLMsarXiv:2501.12599Kimi Team, Angang Du, Bofei Gao, et al.2025 · 431 citations
  2. Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning ↗arXiv:2504.11354 · no summary yetHaiming Wang et al.2025 · 92 citations
  3. Large Language Model-Enhanced Multi-Armed BanditsAnnual Meeting of the Association for Computational Linguistics (ACL)Jiahang Sun, Zhiyong Wang, Runhan Yang, et al.2026 · 1 citation

 Co-authors here

11 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 944 times (of the 149,439 papers whose bibliographies we read); 239 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/chenjun-xiao/