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Louis Castricato

Louis Castricato has 3 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning; often with Alon Albalak, Chase Blagden, Dakota Mahan.

3 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 5 · 32 works · 354 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 185 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7479

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

🎓Georgia Institute of TechnologyAtlanta · United States2021–2022 · 8 papers🎓Brown UniversityProvidence · United States2022–2024 · 3 papers🎓Stanford UniversityStanford · United States2024 · 2 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.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksMathematics · 1 workApplied Mathematics · 1 workComputational Theory and Mathematics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 1 work

 Works by year

2020s

20252 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Generative Reward Models (2024 · 58 citations)
    Reward models trained with the Bradley-Terry objective on human preference pairs score well on familiar data but generalize poorly to unseen tasks.
  2. Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought (2025 · 47 citations)
    Large language models handle routine math well but still fail on harder problems. The authors argue that ordinary Chain-of-Thought (CoT) is part of the cause. Written solutions in training data show only the polished final derivation. They omit the hidden, non-linear work of trying options, checking them and…
  3. Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models (2025 · 43 citations)
    Reinforcement learning for reasoning in LLMs needs math problems whose answers can be checked automatically.

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. Linearly Mapping from Image to Text Space ↗arXiv (Cornell University) · no summary yetJack Merullo et al.2022 · 65 citations
  2. Generative Reward ModelsarXiv:2410.12832Dakota Mahan, Duy Van Phung, Rafael Rafailov, et al.2024 · 58 citations
  3. Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-ThoughtarXiv:2501.04682Violet Xiang, Charlie Snell, Kanishk Gandhi, et al.2025 · 47 citations
  4. Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language ModelsarXiv:2502.17387Alon Albalak, Duy Phung, Nathan Lile, et al.2025 · 43 citations

 Co-authors here

12 of 13 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 185 times (of the 149,439 papers whose bibliographies we read); 73 citing papers with a page here →.

Updated

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