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Lang Cao

Lang Cao has 2 papers in our library, in 2026, most of them on Language models & pretraining, Reasoning; often with Hanbing Liu, Dongmei Zhang, Haoyu Dong.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 10 · 29 works · 374 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #11580

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

🎓The University of TokyoTokyo · Japan2009–2026 · 10 paperscurrent🎓Zhengzhou UniversityZhengzhou · China2016–2020 · 5 papers🎓Fudan UniversityShanghai · China2013–2014 · 4 papers🎓Shanghai Jiao Tong UniversityShanghai · China2006–2008 · 4 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 6The 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 workInformation Systems · 1 work

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

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning (2026 · 1 citation)
    Reasoning LLMs often generate chains of thought much longer than they need to be. Earlier RL methods for shortening them apply length penalties uniformly across tokens, which can hurt answer correctness. This paper treats the problem at the token level. It notes that a large share of CoT tokens barely influence the…
  2. RAG or Learning? Understanding the Limits of LLM Adaptation under Continuous Knowledge Drift in the Real World (2026)
    LLMs learn facts at pretraining time, so they fall out of date as the world changes. They may also mix up older and newer states of the same fact, which the authors call continuous knowledge drift. Continual finetuning, knowledge editing and RAG have rarely been tested on realistic, chronologically ordered streams of…

 Co-authors here

7 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.

Updated

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