Elamur Research⌕
☰
home / authors / Jingcheng Hu

Jingcheng Hu

Jingcheng Hu has 3 papers in our library, in 2025–2026, most of them on Reasoning, Vision & language; often with Chengyuan Yao, Fanqi Wan, Yinmin Zhang.

3 papers in our library · 2025–2026

OpenAlex · h-index 6 · 27 works · 228 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 205 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6372

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

🏥First Affiliated Hospital of Soochow UniversitySuzhou · China2017–2026 · 12 paperscurrent🎓Tsinghua UniversityBeijing · China2023–2026 · 5 paperscurrent🎓Xinjiang Medical UniversityÜrümqi · China2024–2026 · 2 paperscurrent🏥First Affiliated Hospital of Xinjiang Medical UniversityÜrümqi · China2026 · 1 papercurrent🏢Microsoft Research AsiaBeijing · China2023–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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).Computer Science · 3 worksArtificial Intelligence · 3 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 2 works

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 2 worksVision & language · 1 work

 Works by year

2020s

20262 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model (2025 · 128 citations)
    DeepSeek-R1-Zero showed that reinforcement learning applied directly to a base language model can steadily improve reasoning, but its training recipe was only briefly described and not reproducible.
  2. PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning (2026 · 5 citations)
    Language models that reason step by step in a single chain hit a hard limit: once the context window is full, reasoning must stop.
  3. STEP3-VL-10B Technical Report (2026 · 2 citations)
    Compact multimodal models (around 10B parameters or fewer) are usually efficient but weaker at difficult perception and reasoning.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base ModelConference on Neural Information Processing Systems (NeurIPS)Jingcheng Hu, Yinmin Zhang, Qi Han, et al.2025 · 128 citations
  2. Common 7B Language Models Already Possess Strong Math Capabilities ↗arXiv:2403.04706 · no summary yetChen Li et al.2024 · 79 citations
  3. PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Jingcheng Hu, Yinmin Zhang, Shijie Shang, et al.2026 · 5 citations
  4. STEP3-VL-10B Technical ReportarXiv:2601.09668Ailin Huang, Chengyuan Yao, Chunrui Han, et al.2026 · 2 citations

 Co-authors here

12 of 13 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 205 times (of the 149,439 papers whose bibliographies we read); 61 citing papers with a page here →.

Updated

↑ back to top

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