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Jinyang Gao

Jinyang Gao has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning; often with Bolin Ding, Bingbing Xu, Shuchang Tao.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 11 · 29 works · 1,133 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 8 times in our library · ⭐ Paper Stars ranking: #10151

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

🏢Alibaba GroupHangzhou · China2020–2026 · 18 paperscurrent🎓Harbin Institute of TechnologyHarbin · China2025–2026 · 2 paperscurrent🎓National University of SingaporeSingapore · Singapore2014–2015 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 work

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

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Language Adaptation of Large Language Models: An Empirical Study on LLaMA2 (2025 · 7 citations)
    Most large language models learn mainly from English text and handle other languages less well. Building a balanced multilingual model from scratch is too costly for most groups, so adapting an existing model is the practical alternative. Methods for transferring older models such as BERT across languages may not…
  2. Incentivizing Strong Reasoning from Weak Supervision (2026 · 1 citation)
    Improving LLM reasoning usually requires either reinforcement learning with verifiable rewards, which is compute-intensive, or SFT on long chain-of-thought (CoT) data from very strong teachers, which is costly to obtain.

 Co-authors here

6 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 8 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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