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Debing Zhang

Debing Zhang has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining; often with Da Zheng, Hongyu Lin, Jiawei Chen.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 8 · 40 works · 387 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 50 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #11525

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

🎓East China University of Science and TechnologyShanghai · China2020–2026 · 19 paperscurrent🎓Zhejiang UniversityHangzhou · China2012–2014 · 6 papers🎓University of Science and Technology of ChinaHefei · China2014–2017 · 4 papers🏢Xiaohongshu (Little Red Book)Shanghai · China2024–2025 · 3 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 6The 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 workInformation Systems · 1 work

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

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models (2025 · 7 citations)
    Extending large language models to very long contexts is difficult for three reasons. Natural documents of 32K–128K tokens are scarce. Upsampling long data shifts the training distribution and can hurt short-context skills. Attention over long sequences is also expensive. The authors propose Untie the Knots (UtK), an…
  2. MemSearcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement Learning (2026)
    Search agents built on LLMs usually follow the ReAct pattern. Every thought, tool call and retrieved passage is appended to the prompt, so context grows with each turn. The inputs fill up with irrelevant retrieved text, and compute and GPU memory costs rise. MemSearcher replaces this ever-growing history: at each…

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. RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems? ↗arXiv:2501.11284 · no summary yetHaotian Xu et al.2025 · 37 citations
  2. Rethinking Reward Model Evaluation: Are We Barking up the Wrong Tree? ↗arXiv:2410.05584 · no summary yetXueru Wen et al.2024 · 9 citations
  3. Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Junfeng Tian, Da Zheng, Yang Chen, et al.2025 · 7 citations
  4. MemSearcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement LearningFindings of the Association for Computational LinguisticsQianhao Yuan, Jie Lou, Zichao Li, et al.2026

 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 50 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/debing-zhang/