Quentin Gallouédec is a research engineer at Hugging Face with seven years’ experience applying reinforcement learning and transformer-based methods to production-grade ML tooling. He is the lead maintainer on TRL (training transformer language models with RL) and has made substantive back-end contributions to high-profile projects like Hugging Face Transformers and Stable Baselines3, improving trainers, replay buffers, and multi-device support. Quentin holds an engineering degree and a PhD in Machine Learning from École Centrale de Lyon and blends deep research rigor with practical engineering—refactoring dataset handling, loss logic, and memory management for real-world workloads. His background includes robotics and real-time control research (a 3D-printed robotic arm project) and hands-on systems experience from implementing GRPO rewards to fixing subtle prediction-type bugs. He also serves as a reservist innovation officer in the Paris fire brigade, a role that informs his pragmatic approach to resilient system design. Fluent across PyTorch, RL algorithm implementations, and large-model training pipelines, he thrives on making complex research reproducible and production-ready.
7 years of coding experience
Master’s degree, Electronics, Electrical Energy and Automation / Electronics of embedded systems, Master’s degree, Electronics, Electrical Energy and Automation / Electronics of embedded systems at CPE Lyon
Classes préparatoires aux grandes écoles (CPGE), Classes préparatoires aux grandes écoles (CPGE) at Lycée Clemenceau - Nantes
Philosophiæ doctor (Ph.D.), Machine Learning, Philosophiæ doctor (Ph.D.), Machine Learning at École Centrale de Lyon
Train transformer language models with reinforcement learning.
Role in this project:
Back-end Developer
Contributions:10 releases, 1013 reviews, 623 PRs in 2 years 3 months
Contributions summary:Quentin primarily focused on improving the DPO Trainer, addressing a typo in the DPOTrainer's warnings and refactoring parameters. They refactored the code to handle various dataset formats and updated the DPO code by truncating input sequences and padding. The user also made changes to the code related to handling different loss types, indicating proficiency in the core DPO training process.
Contributions:68 reviews, 23 PRs, 31 pushes in 2 months
Contributions summary:Quentin primarily focused on developing and maintaining the GRPO script, a core component of the project. They implemented reward functions, including accuracy and format checks, and integrated the cosine scaled reward function. Furthermore, the user contributed to fixing bugs in the GRPO script and refactoring the code for compatibility. Their work included code modifications and updates to address errors and improve overall functionality.
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Quentin Gallouédec - Research Engineer at Hugging Face