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Michael Santacroce

Michael Santacroce has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Ahmed Hassan Awadallah, Arindam Mitra, Corby Rosset.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 5 · 10 works · 191 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1,044 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9294

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

🎓University of CincinnatiCincinnati · United States2018–2020 · 4 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences (2024 · 44 citations)
    Standard RLHF, and contrastive shortcuts such as DPO, assume that preferences derive from a scalar reward (e.g., the Bradley-Terry model).
  2. 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

3 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. Direct Nash Optimization: Teaching Language Models to Self-Improve with General PreferencesarXiv:2404.03715Corby Rosset, Ching-An Cheng, Arindam Mitra, et al.2024 · 44 citations
  2. Efficient RLHF: Reducing the Memory Usage of PPOarXiv:2309.00754Michael Santacroce, Yadong Lu, Han Yu, et al.2023 · 20 citations
  3. What Matters In The Structured Pruning of Generative Language Models? ↗arXiv (Cornell University) · no summary yetMichael Santacroce et al.2023 · 20 citations

 Co-authors here

8 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 1,044 times (of the 149,439 papers whose bibliographies we read); 223 citing papers with a page here →.

Updated

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