--- title: "Jian Liang — author profile and papers — Elamur Research" description: "Jian Liang, Chinese Academy of Sciences: 2 papers on Elamur Research (2026), h-index 0 — affiliations, topics, co-authors and who cites…" canonical: "https://research.elamur.ai/free/en/authors/jian-liang/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/jian-liang/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Jian Liang # Jian Liang Jian Liang has 2 papers in our library, in 2026, most of them on [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/); often with [Ran He](https://md.research.elamur.ai/free/en/authors/ran-he/), [Dong Yan](https://md.research.elamur.ai/free/en/authors/dong-yan/), [Meng Wang](https://md.research.elamur.ai/free/en/authors/meng-wang/). 2 papers in our library · 2026 [OpenAlex](https://openalex.org/A5125946009) · [**h-index 0**](https://openalex.org/authors/A5125946009) · [8 works](https://openalex.org/works?filter=author.id:A5125946009) · [0 citations](https://openalex.org/works?filter=author.id:A5125946009&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/jian-liang/#papers) · [🔗 Cited 2 times in our library](https://md.research.elamur.ai/free/en/authors/jian-liang/cited-by/) · [⭐ Paper Stars ranking: #12518](https://md.research.elamur.ai/free/en/paper-stars/page/126/#star-12518) 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=Jian+Liang) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Jian+Liang) · [dblp](https://dblp.org/search?q=Jian+Liang) · [ACL Anthology](https://aclanthology.org/search/?q=Jian+Liang) ## Affiliations [🏛️**Chinese Academy of Sciences** Beijing · China 2026 · 7 papers*current*](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) · [🔬**Institute of Automation** Beijing · China 2026 · 7 papers*current*](https://md.research.elamur.ai/free/en/labs/institute-of-automation/) · [🎓**University of Chinese Academy of Sciences** Beijing · China 2026 · 7 papers*current*](https://md.research.elamur.ai/free/en/universities/university-of-chinese-academy-of-sciences/) 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/) · [Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Arts and Humanities · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 1 Research subjects and languages the work is about.[Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/jian-liang/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time](https://md.research.elamur.ai/free/en/papers/yan-2026-what-if-consensus-lies-selective/)** *(2026 · 2 citations)* Test-Time Reinforcement Learning (TTRL) lets LLMs improve their reasoning on unlabeled test questions by using the majority-voted answer among sampled rollouts as a pseudo-reward. 2. **[Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning](https://md.research.elamur.ai/free/en/papers/yu-2026-understanding-mitigating-spurious-signal-amplification/)** *(2026)* Test-time reinforcement learning (TTRL) lets an LLM improve on unlabeled test questions. It samples many answers, takes the majority vote as a pseudo-label, and trains with GRPO. Because rewards come only from the model's own outputs, wrong pseudo-labels introduce misleading training signals. The authors examine… ## Co-authors here 4 co-authors with a page, the most papers together first. - [Ran He](https://md.research.elamur.ai/free/en/authors/ran-he/) ×2 - [Dong Yan](https://md.research.elamur.ai/free/en/authors/dong-yan/) - [Meng Wang](https://md.research.elamur.ai/free/en/authors/meng-wang/) - [Yanbo Wang](https://md.research.elamur.ai/free/en/authors/yanbo-wang/) ## 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 2 times (of the 149,439 papers whose bibliographies we read); [1 citing paper with a page here →](https://md.research.elamur.ai/free/en/authors/jian-liang/cited-by/). Updated 2 November 2026