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Yadong Lu

Yadong Lu has 3 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Yelong Shen, Andres Codas, Arindam Mitra.

3 papers in our library · 2023–2024

OpenAlex · h-index 4 · 17 works · 63 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 122 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7953

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

🔬Institute of Spacecraft System EngineeringBeijing · China2008–2024 · 4 paperscurrent🏛️China Academy of Space TechnologyBeijing · China2008–2024 · 3 paperscurrent🎓East China Normal UniversityShanghai · China2024 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 work

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

 Works by year

2020s

20242 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling (2024 · 59 citations)
    Language models struggle with very long inputs. Full attention scales quadratically with sequence length, while state space models (SSMs) are linear but recall specific details poorly. The authors propose Samba, a hybrid that stacks Mamba layers, SwiGLU MLPs and Sliding Window Attention (SWA, window 2048) in…
  2. AgentInstruct: Toward Generative Teaching with Agentic Flows (2024 · 42 citations)
    Post-training language models on synthetic data is appealing, but such data often lacks quality and variety.
  3. Efficient RLHF: Reducing the Memory Usage of PPO (2023 · 20 citations)
    RLHF with PPO is costly. The PPO stage keeps an actor, a critic, a frozen reference policy and a frozen reward model in memory at once, so it needs more than three times the memory of supervised fine-tuning. That puts it out of reach for many teams. The Microsoft authors measure how several memory-saving variants…

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. Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language ModelingarXiv:2406.07522Liliang Ren, Yang Liu, Yadong Lu, et al.2024 · 59 citations
  2. AgentInstruct: Toward Generative Teaching with Agentic FlowsarXiv:2407.03502Arindam Mitra, Luciano Del Corro, Guoqing Zheng, et al.2024 · 42 citations
  3. Efficient RLHF: Reducing the Memory Usage of PPOarXiv:2309.00754Michael Santacroce, Yadong Lu, Han Yu, et al.2023 · 20 citations
  4. An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models ↗arXiv (Cornell University) · no summary yetYadong Lu et al.2023 · 18 citations

 Co-authors here

12 of 15 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 122 times (of the 149,439 papers whose bibliographies we read); 36 citing papers with a page here →.

Updated

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