🧑‍🔬 Eran Malach

🏛️ Affiliations

🎓Harvard UniversityCambridge · United States2024–2026 · 9 paperscurrent🎓Carnegie Mellon UniversityPittsburgh · United States2026 · 1 papercurrent🎓Hebrew University of JerusalemJerusalem · Israel2017–2023 · 15 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 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. Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining (2025 · 34 citations)
   The paper asks why reinforcement-learning fine-tuning improves mathematical reasoning in language models. Pretraining data for popular base models such as Qwen is usually undisclosed, which makes it hard to separate the effect of RL from the effect of pretraining. To get around this, the authors train decoder-only…
3. Transcendence: Generative Models Can Outperform The Experts That Train Them (2024 · 12 citations)
   Models trained with cross-entropy learn to mimic the distribution of their training data. This suggests they cannot outperform the humans who produced that data. The authors examine this assumption and define "transcendence" as a generative model earning higher reward than every expert behind its training set.

All works

5 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. 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. Echo Chamber: RL Post-training Amplifies Behaviors Learned in PretrainingarXiv:2504.07912Rosie Zhao, Alexandru Meterez, Sham Kakade, et al.2025 · 34 citations
3. LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks ↗Proceedings of the 31st International Conference on Computational Linguistics: Industry Track · no summary yetAkshara Prabhakar et al.2025 · 14 citations
4. Transcendence: Generative Models Can Outperform The Experts That Train ThemConference on Neural Information Processing Systems (NeurIPS)Edwin Zhang, Vincent Zhu, Naomi Saphra, et al.2024 · 12 citations
5. Mixture of Parrots: Experts improve memorization more than reasoning ↗arXiv (Cornell University) · no summary yetSamy Jelassi et al.2024 · 4 citations

🤝 Co-authors here

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

• Sham M. Kakade ×3
• Samy Jelassi ×2
• Benjamin L. Edelman
• David Brandfonbrener
• Naomi Saphra

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Conference on Neural Information Processing Systems (NeurIPS): NeurIPS 2024
• Proceedings of the 31st International Conference on Computational Linguistics: Industry Track

🔗 Cited by

Papers of our library cite their works 111 times (of the 149,439 papers whose bibliographies we read); 36 citing papers with a page here →.

Updated 3 November 2026

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