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Enyu Zhou

Enyu Zhou has 3 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Zhiheng Xi, Limao Xiong, Rui Zheng.

3 papers in our library · 2023–2024

OpenAlex · h-index 7 · 33 works · 1,116 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 501 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #1953

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

🎓Fudan UniversityShanghai · China2023–2026 · 22 paperscurrent🎓Guangzhou Institute of TechnologyGuangzhou · China2026 · 1 papercurrent🎓Xidian UniversityXi'an · China2026 · 1 papercurrent

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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 works

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

💡 Concepts 1Key ideas and methods explained on our concept pages.Large language models · 1 work

 Works by year

2020s

20242 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. The Rise and Potential of Large Language Model Based Agents: A Survey (2023 · 1,272 citations)
    This 2023 survey from the Fudan NLP Group maps the young field of agents built on large language models. The authors argue that earlier agent research mostly tuned algorithms for narrow skills, such as symbolic reasoning or board games like Go and Chess. What the field lacked was a general base model that agents…
  2. LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin (2024 · 93 citations)
    The paper examines a trade-off in supervised fine-tuning (SFT) of LLMs. Adding much more instruction data keeps improving downstream tasks, but it also erodes the factual knowledge the model gained in pre-training. The authors build a 5-million-sample instruction set covering seven task categories and fine-tune…
  3. RMB: Comprehensively Benchmarking Reward Models in LLM Alignment (2024 · 30 citations)
    Reward models (RMs) steer LLM alignment, but existing evaluations such as RewardBench often fail to predict how well an RM actually guides alignment.

All works

6 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. The Rise and Potential of Large Language Model Based Agents: A SurveyScience China Information SciencesZhiheng Xi, Wenxiang Chen, Xin Guo, et al.2023 · 1,272 citations
  2. LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style PluginAnnual Meeting of the Association for Computational Linguistics (ACL)Shihan Dou, Enyu Zhou, Yan Liu, et al.2024 · 93 citations
  3. Secrets of RLHF in Large Language Models Part II: Reward Modeling ↗arXiv:2401.06080 · no summary yetBinghai Wang et al.2024 · 72 citations
  4. StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback ↗Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetShihan Dou et al.2024 · 32 citations
  5. RMB: Comprehensively Benchmarking Reward Models in LLM AlignmentarXiv:2410.09893Enyu Zhou, Guodong Zheng, Binghai Wang, et al.2024 · 30 citations
  6. VRPO: Rethinking Value Modeling for Robust RL under Noisy Supervision in LLM Post-Training ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetDingwei Zhu et al.2026

 Co-authors here

12 of 18 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 501 times (of the 149,439 papers whose bibliographies we read); 106 citing papers with a page here →.

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