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Binghai Wang

Binghai Wang has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning; often with Limao Xiong, Rui Zheng, Shihan Dou.

3 papers in our library · 2023–2026

OpenAlex · h-index 2 · 8 works · 27 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 215 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5984

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 · China2024–2025 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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 2Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20261 paper20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Secrets of RLHF in Large Language Models Part I: PPO (2023 · 135 citations)
    RLHF is the main way to make LLMs helpful, honest and harmless. Its PPO stage, though, is unstable, sensitive to hyperparameters and costly to debug, and few open implementations existed. This report from the Fudan NLP Group and ByteDance breaks the RLHF pipeline into its stages: SFT, reward model training, and PPO…
  2. 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.
  3. Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models (2026 · 3 citations)
    Generative reward models (GenRMs) and LLM-as-a-Judge systems are usually trained and evaluated only on whether they pick the preferred response.

All works

4 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. Secrets of RLHF in Large Language Models Part I: PPOarXiv:2307.04964Rui Zheng, Shihan Dou, Songyang Gao, et al.2023 · 135 citations
  2. Secrets of RLHF in Large Language Models Part II: Reward Modeling ↗arXiv:2401.06080 · no summary yetBinghai Wang et al.2024 · 72 citations
  3. RMB: Comprehensively Benchmarking Reward Models in LLM AlignmentarXiv:2410.09893Enyu Zhou, Guodong Zheng, Binghai Wang, et al.2024 · 30 citations
  4. Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Binghai Wang, Yantao Liu, Yuxuan Liu, et al.2026 · 3 citations

 Co-authors here

12 of 23 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 215 times (of the 149,439 papers whose bibliographies we read); 41 citing papers with a page here →.

Updated

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