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David Brandfonbrener

David Brandfonbrener has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining; often with Eran Malach, Lovish Madaan, Manzil Zaheer.

2 papers in our library · 2024–2025

OpenAlex · h-index 7 · 32 works · 127 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 81 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #8954

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

🎓New York UniversityNew York · United States2019–2023 · 10 papers🎓Harvard UniversityCambridge · United States2024 · 4 papers🎓Yale UniversityNew Haven · United States2018–2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 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

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Repeat After Me: Transformers are Better than State Space Models at Copying (2024 · 57 citations)
    The paper asks what models with a constant-size memory give up in exchange for cheap inference. The authors call this family "generalized state space models" (GSSMs). It includes S4, Mamba, LSTMs and linear-attention variants. The authors find that the main loss is the ability to reproduce or retrieve arbitrary parts…
  2. The Art of Scaling Reinforcement Learning Compute for LLMs (2025 · 20 citations)
    Reinforcement learning now consumes a large share of LLM training compute. Unlike pre-training, though, it has no dependable way to forecast how a training recipe will behave as budgets grow. The authors tackle this with a study totalling over 400,000 GPU-hours on Nvidia GB200 hardware. Most runs use an 8B dense…

All works

4 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. Repeat After Me: Transformers are Better than State Space Models at CopyingarXiv:2402.01032Samy Jelassi, David Brandfonbrener, Sham M. Kakade, et al.2024 · 57 citations
  2. The Art of Scaling Reinforcement Learning Compute for LLMsarXiv:2510.13786Devvrit Khatri, Lovish Madaan, Rishabh Tiwari, et al.2025 · 20 citations
  3. Evaluating representations by the complexity of learning low-loss predictors ↗arXiv (Cornell University) · no summary yetWhitney, William F. et al.2020 · 13 citations
  4. Mixture of Parrots: Experts improve memorization more than reasoning ↗arXiv (Cornell University) · no summary yetSamy Jelassi et al.2024 · 4 citations

 Co-authors here

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

 Cited by

Papers of our library cite their works 81 times (of the 149,439 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated

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