--- title: "Chenyao Lu — author profile and papers — Elamur Research" description: "Chenyao Lu, Tencent: 2 papers on Elamur Research (2026), h-index 0 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/chenyao-lu/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/chenyao-lu/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Chenyao Lu # Chenyao Lu Chenyao Lu has 2 papers in our library, in 2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/); often with [Chao Xue](https://md.research.elamur.ai/free/en/authors/chao-xue/), [Di Liang](https://md.research.elamur.ai/free/en/authors/di-liang/), [Haibo Shi](https://md.research.elamur.ai/free/en/authors/haibo-shi/). 2 papers in our library · 2026 [OpenAlex](https://openalex.org/A5133585249) · [**h-index 0**](https://openalex.org/authors/A5133585249) · [6 works](https://openalex.org/works?filter=author.id:A5133585249) · [0 citations](https://openalex.org/works?filter=author.id:A5133585249&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/chenyao-lu/#papers) · [🔗 Cited 14 times in our library](https://md.research.elamur.ai/free/en/authors/chenyao-lu/cited-by/) · [⭐ Paper Stars ranking: #9533](https://md.research.elamur.ai/free/en/paper-stars/page/96/#star-9533) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Chenyao+Lu) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Chenyao+Lu) · [dblp](https://dblp.org/search?q=Chenyao+Lu) · [ACL Anthology](https://aclanthology.org/search/?q=Chenyao+Lu) ## Affiliations [🏢**Tencent** Shenzhen · China 2026 · 6 papers*current*](https://md.research.elamur.ai/free/en/companies/tencent/) 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 4 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 2 Research subjects and languages the work is about.[Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/chenyao-lu/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty](https://md.research.elamur.ai/free/en/papers/xue-2026-reason-only-when-needed-efficient/)** *(2026 · 8 citations)* Generative reward models (GRMs) judge LLM outputs using chain-of-thought (CoT) reasoning. They have two weaknesses. They spend reasoning effort on every input, including easy ones. They also pick among sampled reasoning chains by voting, which cannot separate sound chains from ones that only look convincing. The… 2. **[Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models](https://md.research.elamur.ai/free/en/papers/xue-2026-why-supervised-fine-tuning-fails/)** *(2026 · 6 citations)* Supervised fine-tuning (SFT) is usually treated as a reliable way to specialize LLMs. The authors show, however, that converged models still get part of their own training set wrong. They call this the Incomplete Learning Phenomenon (ILP), distinguish it from catastrophic forgetting and machine unlearning, and… ## Co-authors here 11 co-authors with a page, the most papers together first. - [Chao Xue](https://md.research.elamur.ai/free/en/authors/chao-xue/) ×2 - [Di Liang](https://md.research.elamur.ai/free/en/authors/di-liang/) ×2 - [Haibo Shi](https://md.research.elamur.ai/free/en/authors/haibo-shi/) ×2 - [Lei Jiang](https://md.research.elamur.ai/free/en/authors/lei-jiang/) ×2 - [Mengqiao Liu](https://md.research.elamur.ai/free/en/authors/mengqiao-liu/) ×2 - [Peiyang Liu](https://md.research.elamur.ai/free/en/authors/peiyang-liu/) ×2 - [Shuang Liang](https://md.research.elamur.ai/free/en/authors/shuang-liang/) ×2 - [Xianjie Wu](https://md.research.elamur.ai/free/en/authors/xianjie-wu/) ×2 - [Xingsheng Han](https://md.research.elamur.ai/free/en/authors/xingsheng-han/) ×2 - [Yao Wang](https://md.research.elamur.ai/free/en/authors/yao-wang/) ×2 - [Yu Lu](https://md.research.elamur.ai/free/en/authors/yu-lu/) ×2 ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/): [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) - [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) ## Cited by Papers of our library cite their works 14 times (of the 149,439 papers whose bibliographies we read); [6 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/chenyao-lu/cited-by/). Updated 30 October 2026