Présentation de la société : CEA
Le CEA est un acteur majeur de la recherche, au service des citoyens, de l'économie et de l'Etat.Il apporte des solutions concrètes à leurs besoins dans quatre domaines principaux : transition énergétique, transition numérique, technologies pour la médecine du futur, défense et sécurité sur un socle de recherche fondamentale. Le CEA s'engage depuis plus de 75 ans au service de la souveraineté scientifique, technologique et industrielle de la France et de l'Europe pour un présent et un avenir mieux maîtrisés et plus sûrs.
Implanté au cœur des territoires équipés de très grandes infrastructures de recherche, le CEA dispose d'un large éventail de partenaires académiques et industriels en France, en Europe et à l'international.
Les 20 000 collaboratrices et collaborateurs du CEA partagent trois valeurs fondamentales :
• La conscience des responsabilités
• La coopération
• La curiosité
Missions
As an intern at the CEA, you will have the opportunity to work within a world-renowned research environment. Our teams consist of passionate and dedicated experts, providing an ideal setting for learning and collaboration. You will have access to state-of-the-art equipment and top-tier research resources to carry out your assignments. The work conducted may potentially lead to a scientific publication.
Context :
The team trains vision-language models (VLMs) that read an image of a table and output its structure. Manual table annotation is expensive, which limits classical supervised approaches.
This internship focuses on training VLMs with reinforcement learning (GRPO) , using reward signals that do not depend on human annotations: for example, checking the geometric consistencies or generating synthetic tables whose structure is known by construction. The central question is which annotation-free signals correlate well with the real quality of a prediction, and how to combine them to improve the model on real documents.
What do we expect from you?
Get started with the team's training and evaluation environment
Analyse automatic evaluation methods
Propose and implement new approaches, and validate them through experiments
Work on the performance of the pipeline and the robustness of the models
Evaluate the models on public benchmarks
Document the results
The intern works on an existing pipeline, with access to a GPU cluster (Python, PyTorch, HuggingFace ecosystem, distributed training, experiment tracking) . Supervision research engineers of the team, with a weekly meeting.
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Profil recherché
Students in their final year of studies (M2 or engineering school)
Python and PyTorch, Linux
Knowledge of deep learning and statistics
Good level of English
Appreciated: experience with HuggingFace Transformers, notions of reinforcement learning (GRPO, DPO, …)