What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study. - Marcin Andrychowicz; Anton Raichuk; Piotr Stanczyk; Manu Orsini; Sertan Girgin; Raphaël Marinier; Léonard Hussenot; Matthieu Geist; Olivier Pietquin; Marcin Michalski; Sylvain Gelly; Olivier Bachem (CoRR, 2020)

What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study. - Marcin Andrychowicz; Anton Raichuk; Piotr Stanczyk; Manu Orsini; Sertan Girgin; Raphaël Marinier; Léonard Hussenot; Matthieu Geist; Olivier Pietquin; Marcin Michalski; Sylvain Gelly; Olivier Bachem (ICLR, 2021)

Hyperparameter Selection for Imitation Learning. - Léonard Hussenot; Marcin Andrychowicz; Damien Vincent; Robert Dadashi; Anton Raichuk; Sabela Ramos; Nikola Momchev; Sertan Girgin; Raphaël Marinier; Lukasz Stafiniak; Manu Orsini; Olivier Bachem; et al. (ICML, 2021)

What Matters for Adversarial Imitation Learning? - Manu Orsini; Anton Raichuk; Léonard Hussenot; Damien Vincent; Robert Dadashi; Sertan Girgin; Matthieu Geist; Olivier Bachem; Olivier Pietquin; Marcin Andrychowicz (NeurIPS, 2021)

C3PO: Learning to Achieve Arbitrary Goals via Massively Entropic Pretraining. - Alexis Jacq; Manu Orsini; Gabriel Dulac-Arnold; Olivier Pietquin; Matthieu Geist; Olivier Bachem (CoRR, 2022)

Moshi: a speech-text foundation model for real-time dialogue. - Alexandre Défossez; Laurent Mazaré; Manu Orsini; Amélie Royer; Patrick Pérez; Hervé Jégou; Edouard Grave; Neil Zeghidour (CoRR, 2024)

Live Music Models. - Antoine Caillon; Brian McWilliams; Cassie Tarakajian; Ian Simon; Ilaria Manco; Jesse H. Engel; Noah Constant; Yunpeng Li 008; Timo I. Denk; Alberto Lalama; Andrea Agostinelli; Cheng-Zhi Anna Huang; et al. (CoRR, 2025)