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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.

2 papers in our library · 2025–2026

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

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

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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. 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…
  2. 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

11 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 47 times (of the 149,439 papers whose bibliographies we read); 22 citing papers with a page here →.

Updated

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