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Mengshu Sun

Mengshu Sun has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Efficient models & compression; often with Bohan Zhang, Da Zheng, Huajun Chen.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 10 · 44 works · 231 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 49 times in our library · ⭐ Paper Stars ranking: #7710

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

🏢Ant GroupHangzhou · China2022–2026 · 21 paperscurrent🎓Zhejiang UniversityHangzhou · China2024–2026 · 4 paperscurrent🎓Communication University of ChinaBeijing · China2009 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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 · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Efficient models & compression · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. LightThinker: Thinking Step-by-Step Compression (2025 · 50 citations)
    Slow, o1-style reasoning makes LLMs produce very long token sequences. Because attention cost grows quadratically and the KV Cache grows linearly with context, these outputs are expensive in memory and time. LightThinker addresses this by teaching a model to condense each intermediate reasoning step as it goes. After…
  2. Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts (2026 · 4 citations)
    Long reasoning rollouts in LLM reinforcement learning store large KV caches. This memory cost limits batch sizes and GPU utilization, especially since rollout generation takes most of the training time. The authors ask whether training-free KV compression, built for inference, can be used during rollouts. They find…

 Co-authors here

12 of 15 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 49 times (of the 149,439 papers whose bibliographies we read); 19 citing papers with a page here →.

Updated

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