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Jean Mercat

Jean Mercat has 3 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning; often with Sedrick Keh, Georgios Smyrnis, Marianna Nezhurina.

3 papers in our library · 2024–2025

OpenAlex · h-index 3 · 8 works · 38 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 352 times in our library · ⭐ Paper Stars ranking: #5834

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

🔬Toyota Research InstitutePalo Alto · United States2024–2026 · 3 paperscurrent🔬Laboratoire de Chimie des Polymères OrganiquesPessac · France2016–2018 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 worksMathematics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 1 workLanguage models & pretraining · 2 works

 Works by year

2020s

20251 paper20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. OpenThoughts: Data Recipes for Reasoning Models (2025 · 122 citations)
    The strongest reasoning models are usually distilled from training data that is not public, so researchers know little about which data choices actually matter.
  2. Language models scale reliably with over-training and on downstream tasks (2024 · 46 citations)
    Scaling laws let teams forecast large language model behaviour from cheap small runs. Most published laws, however, assume Chinchilla-style compute-optimal training and predict only next-token loss. In practice, smaller models are often over-trained on far more tokens to cut inference cost, and they are compared on…
  3. Linearizing Large Language Models (2024 · 16 citations)
    Linear-attention models and other recurrent models keep a fixed-size state during generation, which makes inference cheaper than in softmax transformers.

 Co-authors here

12 of 17 co-authors with a page, the most papers together first.

Venues

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

 Cited by

Papers of our library cite their works 352 times (of the 149,439 papers whose bibliographies we read); 72 citing papers with a page here →.

Updated

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