🧑‍🔬 Quanquan Gu

🏛️ Affiliations

🎓University of California, Los AngelesLos Angeles · United States2018–2026 · 177 paperscurrent🎓University of VirginiaCharlottesville · United States2015–2019 · 40 papers🎓University of Illinois Urbana-ChampaignUrbana · United States2011–2015 · 28 papers🎓Tsinghua UniversityBeijing · China2008–2010 · 18 papers🔬State Key Laboratory of Intelligent Technology and SystemsBeijing · China2009–2010 · 8 papers🏢ByteDanceBeijing · China2023–2025 · 7 papers🎓Princeton UniversityPrinceton · United States2014–2023 · 4 papers🎓University of Maryland, College ParkCollege Park · United States2024–2025 · 2 papers🎓Emory UniversityAtlanta · United States2024 · 2 papers🎓Toyota Technological Institute at ChicagoChicago · United States2019–2021 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 14 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.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves (2023 · 62 citations)
   LLMs sometimes misread questions that look perfectly clear to people. The authors attribute this to a mismatch between how humans and models frame a query. For example, GPT-4 may interpret "even month" to mean any month whose number of days is even. To address this, they propose Rephrase and Respond (RaR), a…
2. Self-Distilled Policy Gradient (2026 · 5 citations)
   RLVR methods such as GRPO give a language model a single correctness score per sequence, so individual reasoning steps receive no specific credit.

All works

9 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. Multiple models for outbreak decision support in the face of uncertainty ↗Proceedings of the National Academy of Sciences · no summary yetKatriona Shea et al.2023 · 75 citations
2. Rephrase and Respond: Let Large Language Models Ask Better Questions for ThemselvesarXiv:2311.04205Yihe Deng, Weitong Zhang, Zixiang Chen, et al.2023 · 62 citations
3. Towards Understanding the Mixture-Of-Experts Layer in Deep Learning ↗neural information processing systems · no summary yetZixiang Chen et al.2022 · 49 citations
4. Towards Understanding Mixture of Experts in Deep Learning ↗arXiv (Cornell University) · no summary yetZixiang Chen et al.2022 · 37 citations
5. Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization ↗arXiv (Cornell University) · no summary yetDifan Zou et al.2021 · 13 citations
6. Self-Distilled Policy GradientarXiv:2606.04036Yifeng Liu, Shiyuan Zhang, Yifan Zhang, et al.2026 · 5 citations
7. The Benefits of Mixup for Feature Learning ↗arXiv (Cornell University) · no summary yetDifan Zou et al.2023 · 3 citations
8. The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks ↗arXiv (Cornell University) · no summary yetYuan Cao et al.2023
9. Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP ↗arXiv (Cornell University) · no summary yetZixiang Chen et al.2023

🤝 Co-authors here

3 co-authors with a page, the most papers together first.

• Yifan Zhang
• Yifeng Liu
• Yihe Deng

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Proceedings of the National Academy of Sciences
• neural information processing systems

🔗 Cited by

Papers of our library cite their works 91 times (of the 149,439 papers whose bibliographies we read); 14 citing papers with a page here →.

Updated 3 November 2026

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