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Silas Alberti

Silas Alberti has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining, GPU & parallel computing; often with Christopher Ré, James Zou, Michael Zhang.

2 papers in our library · 2024–2025

OpenAlex · ORCID · Wikidata · h-index 4 · 10 works · 121 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 124 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7456

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

🎓Stanford UniversityStanford · United States2022–2026 · 7 papersEducation since 2022 · PhD, AIcurrent🎓Ludwig-Maximilians-Universität MünchenMunich · Germany2019–2023 · 1 paperEducation 2019–2022 · Bachelor of Science, Mathematics
🏛️Cognition AIUnited StatesEmployment

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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksHardware and Architecture · 1 workInformation Systems · 1 workArts and Humanities · 1 workArtificial Intelligence · 1 workComputer Vision and Pattern Recognition · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.GPU & parallel computing · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Kevin: Multi-Turn RL for Generating CUDA Kernels (2025 · 69 citations)
    The paper asks whether reinforcement learning can teach an LLM to write fast, correct CUDA kernels, given that kernel writing is iterative: engineers revise code after seeing compile errors, test results and timings.
  2. Simple linear attention language models balance the recall-throughput tradeoff (2024 · 59 citations)
    Attention-based models are good at recalling information that has already appeared in the context (recall).

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. Kevin: Multi-Turn RL for Generating CUDA KernelsarXiv 2025Carlo Baronio, Pietro Marsella, Ben Pan, et al.2025 · 69 citations
  2. Simple linear attention language models balance the recall-throughput tradeoffarXiv:2402.18668Simran Arora, Sabri Eyuboglu, Michael Zhang, et al.2024 · 59 citations
  3. Data Unlearning in Diffusion Models ↗arXiv (Cornell University) · no summary yetSilas Alberti et al.2025 · 1 citation

 Co-authors here

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

 Cited by

Papers of our library cite their works 124 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

Updated

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