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Yingwei MA

Yingwei MA has 3 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning; often with Binhua Li, Fei Huang, Rongyu Cao.

3 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 7 · 33 works · 160 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 10 times in our library · ⭐ Paper Stars ranking: #9900

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

🏢Alibaba GroupHangzhou · China2024–2026 · 8 paperscurrent🎓Changchun Medical CollegeChangchun · China2026 · 1 papercurrent🏥Changchun Children's HospitalChangchun · China2019–2024 · 6 papers🎓National University of Defense TechnologyChangsha · China2023 · 4 papers🏢Moonshot AIBeijing · China2025 · 2 papers

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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 2 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 workSoftware · 1 workComputer Vision and Pattern Recognition · 1 work

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

 Works by year

2020s

20262 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization (2026 · 6 citations)
    RL-PLUS targets a known weakness of Reinforcement Learning with Verifiable Reward (RLVR). Standard on-policy training such as GRPO raises pass@1 but often lowers pass@k at large k. In other words, the tuned model solves fewer distinct problems than its base model, which the authors call capability boundary collapse.…
  2. Kimi-Dev: Agentless Training as Skill Prior for SWE-Agents (2025 · 4 citations)
    Automated GitHub issue fixing, as measured by SWE-bench, has split into two camps. Multi-turn SWE-Agents are flexible but hard to train end-to-end. Pipeline-style Agentless methods are easy to optimize with verifiable rewards but rigid. The authors argue the two approaches can be combined: training on Agentless…
  3. Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format (2026)
    Parallel test-time scaling (TTS) usually samples many answers using a single reasoning format chosen by a human.

 Co-authors here

12 of 20 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 10 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated

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