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Limao Xiong

Limao Xiong has 3 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Shihan Dou, Wei Shen, Binghai Wang.

3 papers in our library · 2023–2024

OpenAlex · h-index 6 · 19 works · 1,042 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 553 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5246

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 · China2022–2025 · 9 paperscurrent🏢MeituanBeijing · China2024–2025 · 2 paperscurrent

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 · 2 works

 Works by year

2020s

20242 papers20231 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. 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

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. 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. 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
  4. RMB: Comprehensively Benchmarking Reward Models in LLM AlignmentarXiv:2410.09893Enyu Zhou, Guodong Zheng, Binghai Wang, et al.2024 · 30 citations

 Co-authors here

12 of 15 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 553 times (of the 149,439 papers whose bibliographies we read); 119 citing papers with a page here →.

Updated

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