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Rongxiang Weng

Rongxiang Weng has 3 papers in our library, in 2026, most of them on Language models & pretraining, Question answering; often with Jingang Wang, Xunliang Cai, Can Ren.

3 papers in our library · 2026

OpenAlex · h-index 12 · 76 works · 1,298 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 479 times in our library · ⭐ Paper Stars ranking: #11948

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

🏢MeituanBeijing · China2024–2026 · 28 paperscurrent🎓Shanghai Jiao Tong UniversityShanghai · China2026 · 1 papercurrent🎓University of Chinese Academy of SciencesBeijing · China2026 · 1 papercurrent🏢Alibaba GroupHangzhou · China2020–2022 · 7 papers🎓Nanjing UniversityNanjing · China2017–2020 · 5 papers🎓Soochow UniversitySuzhou · China2022–2023 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 3 worksLanguage and Linguistics · 3 works

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

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Unlocking Implicit Experience: Synthesizing Tool-Use Trajectories from Text (2026 · 4 citations)
    Training LLM agents for multi-turn tool use is limited by a shortage of realistic trajectories. Most existing synthesis methods start from a fixed API collection, so the data can only be as broad as that collection. The authors invert this approach. They argue that ordinary web text, such as how-to guides and…
  2. Large-Scale Diverse Synthesis for Mid-Training (2026)
    Mid-training large language models often relies on synthetic question–answer data. However, existing generators mostly reformat source text into simple, uniform QA pairs, which limits both volume and variety. The authors present BoostQA, a pipeline that first diagnoses what a pre-trained model lacks and then…
  3. LinkQA: Synthesizing Diverse QA from Multiple Seeds Strongly Linked by Knowledge Points (2026)
    Training LLMs is increasingly limited by a shortage of diverse, high-quality data, especially across many academic disciplines.

 Co-authors here

10 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 479 times (of the 149,439 papers whose bibliographies we read); 134 citing papers with a page here →.

Updated

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