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Rong Jin

Rong Jin has 2 papers in our library, in 2026, most of them on Vision & language, Language models & pretraining, Reasoning; often with Chaoyou Fu, Feng Li, Kun Wang.

2 papers in our library · 2026

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

📄 2 works in our library · ⭐ Paper Stars ranking: #13875

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

🏢MetaMenlo Park · United States2026 · 3 paperscurrent🏢Alibaba DAMO AcademyHangzhou · China2026 · 2 paperscurrent🏢Alibaba GroupHangzhou · China2026 · 2 paperscurrent🎓California State University, FullertonFullerton · United States2026 · 1 papercurrent

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 6The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksComputer Vision and Pattern Recognition · 2 worksArtificial Intelligence · 1 workArts and Humanities · 1 workInformation Systems · 1 workLanguage and Linguistics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Vision & language · 2 worksLanguage models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation (2026)
    Current multimodal recommenders fuse images and text well, but they decide in one opaque forward pass. They cannot recognise when they are unsure and spend equal compute on easy and hard queries. ReasonRec, from Meta AI with Michigan State and UNC Chapel Hill collaborators, treats recommendation as an agent loop with…
  2. Scaling Law for Multimodal Large Language Model Supervised Fine-Tuning (2026)
    The paper addresses a gap in multimodal LLM development: there is no established way to estimate how much supervised fine-tuning (SFT) data a vision-language model needs for a given model size or performance target.

 Co-authors here

12 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

This page in Markdown → https://md.research.elamur.ai/free/en/authors/rong-jin/