Denghui Zhang
Denghui Zhang has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning; often with Bowen Jin, Cheng Qian, Hanghang Tong.
OpenAlex · ORCID · h-index 14 · 82 works · 607 citations OpenAlex, 2026-10-14
📄 2 works in our library · 🔗 Cited 47 times in our library · ⭐ Paper Stars ranking: #7831
Affiliations
🎓Stevens Institute of TechnologyHoboken ·
United States2023–2026 · 11 papers🎓Qilu University of TechnologyJinan ·
China2024–2026 · 4 papers🎓Shandong Academy of SciencesJinan ·
China2024–2026 · 4 papers🎓Zhejiang Shuren UniversityHangzhou ·
China2005–2025 · 26 papers🎓Rutgers, The State University of New JerseyNew Brunswick ·
United States2019–2023 · 9 papers🏛️Chinese Academy of SciencesBeijing ·
China2018–2023 · 5 papers🎓Harbin Institute of TechnologyHarbin ·
China2016–2021 · 4 papers🔬Shanghai Institute of Microsystem and Information TechnologyShanghai ·
China2020–2023 · 3 papers🎓University of Chinese Academy of SciencesBeijing ·
China2020–2023 · 3 papers🎓Zhejiang UniversityHangzhou ·
China2006–2008 · 3 papers
Fields, topics and concepts
📚 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 · 1 workLanguage models & pretraining · 1 work
Works by year
2020s
Papers
- RM-R1: Reward Modeling as Reasoning (2025 · 49 citations)
Reward models guide the alignment of LLMs, but each existing family has a weakness. Scalar RMs give no justification for their scores. Generative RMs explain their verdicts, yet their reasoning is often shallow. The authors treat preference judgment as a reasoning problem and introduce Reasoning Reward Models… - Accelerating LLM Fine-Tuning via Embedding Knowledge Transfer (2026)
Fine-tuning large language models for a new domain is costly. PEFT methods such as LoRA update only Transformer block weights and ignore the embedding layer. Weak-to-Strong approaches need pseudo-labels, which require running inference with a small model and keeping both models in GPU memory. The authors propose…
Co-authors here
- Bowen Jin
- Cheng Qian
- Hanghang Tong
- Heng Ji
- Hongru Wang
- Tong Zhang
- Xiusi Chen
- Yi Zhang
- Yu Wang
- Yu Zhang
- Ziqi Wang
Venues
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
Cited by
Updated
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