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Chong Luo

Chong Luo has 2 papers in our library, in 2025–2026, most of them on Reasoning, Language models & pretraining; often with Tian Xie, Bryan Dai, Dongyan Zhao.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 29 · 104 works · 5,365 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 248 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #7847

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

🏢Microsoft Research AsiaBeijing · China2003–2026 · 75 papersEmployment since 2003 · Sr. Principal Research Manager, Visual Computing Groupcurrent🏢MicrosoftRedmond · United States2006–2026 · 12 paperscurrent🎓Shanghai Jiao Tong UniversityShanghai · China2006–2012 · 6 papersEducation 2006–2012 · Ph.D., Electrical Engineering🎓University of Science and Technology of ChinaHefei · China2018–2024 · 2 papers🎓National University of SingaporeSingapore · Singapore2000–2002Education 2000–2002 · M.Sc., Computer Science🎓Fudan UniversityShanghai · China1996–2000Education 1996–2000 · B.Sc., Computer Science

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning (2025 · 104 citations)
    Logic-RL tests whether the rule-based reinforcement learning (RL) approach behind DeepSeek-R1 can be reproduced with a smaller model, and what training data supports it best.
  2. Shorten After You're Right: Lazy Length Penalties for Reasoning RL (2026)
    Reasoning models trained with rule-based on-policy reinforcement learning tend to produce longer and longer outputs.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetDacheng Yin et al.2020 · 327 citations
  2. SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking ↗arXiv:1904.04452 · no summary yetGuangting Wang et al.2019 · 264 citations
  3. Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement LearningarXiv:2502.14768Tian Xie, Zitian Gao, Qingnan Ren, et al.2025 · 104 citations
  4. Shorten After You're Right: Lazy Length Penalties for Reasoning RLFindings of the Association for Computational LinguisticsDanlong Yuan, Tian Xie, Shaohan Huang, et al.2026

 Co-authors here

7 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 248 times (of the 149,439 papers whose bibliographies we read); 81 citing papers with a page here →.

Updated

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