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Zhaoyang Zhang

Zhaoyang Zhang has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Efficient models & compression, Reasoning; often with Dhananjay Ram, Kaipeng Zhang, Mengzhao Chen.

2 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 13 · 23 works · 434 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 193 times in our library · ⭐ Paper Stars ranking: #6648

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

🎓Jiangsu UniversityZhenjiang · China2019–2025 · 8 paperscurrent🎓National University of Defense TechnologyChangsha · China2012–2016 · 12 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 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 3Research subjects and languages the work is about.Efficient models & compression · 2 worksLanguage models & pretraining · 2 worksReasoning · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models (2023 · 195 citations)
    Compressing large language models to very low bit-widths usually forces a trade-off. Post-training quantization (PTQ) is cheap, but it relies on hand-tuned parameters and degrades badly at settings such as W2A16 or W4A4. Quantization-aware training (QAT) keeps accuracy but needs heavy compute and data. OmniQuant aims…
  2. Reinforcement-aware Knowledge Distillation for LLM Reasoning (2026 · 13 citations)
    Long chain-of-thought reasoning models trained with RL are expensive to run, so practitioners distill them into smaller students.

 Co-authors here

12 of 14 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 193 times (of the 149,439 papers whose bibliographies we read); 32 citing papers with a page here →.

Updated

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