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Minxuan Lv

Minxuan Lv has 3 papers in our library, in 2026, most of them on Reasoning; often with Leiyu Pan, Zhenpeng Su, Guorui Zhou.

3 papers in our library · 2026

OpenAlex · h-index 2 · 9 works · 7 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 8 times in our library · ⭐ Paper Stars ranking: #10110

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

🎓University of Chinese Academy of SciencesBeijing · China2023–2026 · 5 paperscurrent🏢KuaishouBeijing · China2025–2026 · 4 paperscurrent🏛️Chinese Academy of SciencesBeijing · China2023 · 2 papers🔬Institute of Information EngineeringBeijing · China2023 · 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 · 3 worksDecision Sciences · 1 workInformation Systems · 1 workManagement Science and Operations Research · 1 workArts and Humanities · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1 work

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. CE-GPPO: Coordinating Entropy via Gradient-Preserving Clipping Policy Optimization in Reinforcement Learning (2026 · 7 citations)
    The paper tackles unstable policy entropy when large language models are trained with RL for reasoning. PPO-style methods, including GRPO and DAPO, clip the importance sampling ratio, and this silently drops gradients from low-probability tokens. The authors split tokens by advantage sign and probability. They argue…
  2. Entropy Ratio Clipping as a Soft Global Constraint for Stable Reinforcement Learning (2026 · 1 citation)
    LLM post-training with reinforcement learning is usually off-policy, so updates use samples from older policies.
  3. Good Reasoning Makes Good Demonstrations: Implicit Reasoning Quality Supervision via In-Context Reinforcement Learning (2026)
    RLVR rewards every correct answer equally, so a model may also be reinforced for sloppy or lucky reasoning that happens to end in the right result.

 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 8 times (of the 149,439 papers whose bibliographies we read); 4 citing papers with a page here →.

Updated

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