On Connections between Constrained Optimization and Reinforcement Learning. - Nino Vieillard; Olivier Pietquin; Matthieu Geist (CoRR, 2019)

Deep Conservative Policy Iteration. - Nino Vieillard; Olivier Pietquin; Matthieu Geist (AAAI, 2020)

Momentum in Reinforcement Learning. - Nino Vieillard; Bruno Scherrer; Olivier Pietquin; Matthieu Geist (AISTATS, 2020)

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning. - Nino Vieillard; Tadashi Kozuno; Bruno Scherrer; Olivier Pietquin; Rémi Munos; Matthieu Geist (NeurIPS, 2020)

Munchausen Reinforcement Learning. - Nino Vieillard; Olivier Pietquin; Matthieu Geist (NeurIPS, 2020)

Leverage the Average: an Analysis of Regularization in RL. - Nino Vieillard; Tadashi Kozuno; Bruno Scherrer; Olivier Pietquin; Rémi Munos; Matthieu Geist (CoRR, 2020)

Offline Reinforcement Learning with Pseudometric Learning. - Robert Dadashi; Shideh Rezaeifar; Nino Vieillard; Léonard Hussenot; Olivier Pietquin; Matthieu Geist (ICML, 2021)

Offline Reinforcement Learning as Anti-exploration. - Shideh Rezaeifar; Robert Dadashi; Nino Vieillard; Léonard Hussenot; Olivier Bachem; Olivier Pietquin; Matthieu Geist (AAAI, 2022)

Implicitly Regularized RL with Implicit Q-values. - Nino Vieillard; Marcin Andrychowicz; Anton Raichuk; Olivier Pietquin; Matthieu Geist (AISTATS, 2022)

KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. - Tadashi Kozuno; Wenhao Yang; Nino Vieillard; Toshinori Kitamura; Yunhao Tang; Jincheng Mei; Pierre Ménard; Mohammad Gheshlaghi Azar; Michal Valko; Rémi Munos; Olivier Pietquin; Matthieu Geist; et al. (CoRR, 2022)

Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. - Paul Roit; Johan Ferret; Lior Shani; Roee Aharoni; Geoffrey Cideron; Robert Dadashi; Matthieu Geist; Sertan Girgin; Léonard Hussenot; Orgad Keller; Nikola Momchev; Sabela Ramos Garea; et al. (ACL (1), 2023)

Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. - Toshinori Kitamura; Tadashi Kozuno; Yunhao Tang; Nino Vieillard; Michal Valko; Wenhao Yang; Jincheng Mei; Pierre Ménard; Mohammad Gheshlaghi Azar; Rémi Munos; Olivier Pietquin; Matthieu Geist; et al. (ICML, 2023)

GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models. - Rishabh Agarwal; Nino Vieillard; Piotr Stanczyk; Sabela Ramos; Matthieu Geist; Olivier Bachem (CoRR, 2023)

On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes. - Rishabh Agarwal; Nino Vieillard; Yongchao Zhou; Piotr Stanczyk; Sabela Ramos Garea; Matthieu Geist; Olivier Bachem (ICLR, 2024)

WARM: On the Benefits of Weight Averaged Reward Models. - Alexandre Ramé; Nino Vieillard; Léonard Hussenot; Robert Dadashi; Geoffrey Cideron; Olivier Bachem; Johan Ferret (ICML, 2024)

Imitating Language via Scalable Inverse Reinforcement Learning. - Markus Wulfmeier; Michael Bloesch; Nino Vieillard; Arun Ahuja; Jörg Bornschein; Sandy H. Huang; Artem Sokolov; Matt Barnes 001; Guillaume Desjardins; Alex Bewley; Sarah Bechtle; Jost Tobias Springenberg; et al. (NeurIPS, 2024)

WARP: On the Benefits of Weight Averaged Rewarded Policies. - Alexandre Ramé; Johan Ferret; Nino Vieillard; Robert Dadashi; Léonard Hussenot; Pierre-Louis Cedoz; Pier Giuseppe Sessa; Sertan Girgin; Arthur Douillard; Olivier Bachem (CoRR, 2024)

BOND: Aligning LLMs with Best-of-N Distillation. - Pier Giuseppe Sessa; Robert Dadashi; Léonard Hussenot; Johan Ferret; Nino Vieillard; Alexandre Ramé; Bobak Shahriari; Sarah Perrin; Abe Friesen; Geoffrey Cideron; Sertan Girgin; Piotr Stanczyk; et al. (CoRR, 2024)

Gemma 2: Improving Open Language Models at a Practical Size. - Morgane Rivière; Shreya Pathak; Pier Giuseppe Sessa; Cassidy Hardin; Surya Bhupatiraju; Léonard Hussenot; Thomas Mesnard; Bobak Shahriari; Alexandre Ramé; Johan Ferret; Peter Liu; Pouya Tafti; et al. (CoRR, 2024)

BOND: Aligning LLMs with Best-of-N Distillation. - Pier Giuseppe Sessa; Robert Dadashi-Tazehozi; Léonard Hussenot; Johan Ferret; Nino Vieillard; Alexandre Ramé; Bobak Shahriari; Sarah Perrin; Abram L. Friesen; Geoffrey Cideron; Sertan Girgin; Piotr Stanczyk; et al. (ICLR, 2025)

Loss Functions and Operators Generated by f-Divergences. - Vincent Roulet; Tianlin Liu; Nino Vieillard; Michael E. Sander; Mathieu Blondel (CoRR, 2025)

On Teacher Hacking in Language Model Distillation. - Daniil Tiapkin; Daniele Calandriello; Johan Ferret; Sarah Perrin; Nino Vieillard; Alexandre Ramé; Mathieu Blondel (CoRR, 2025)

Gemma 3 Technical Report. - Aishwarya Kamath; Johan Ferret; Shreya Pathak; Nino Vieillard; Ramona Merhej; Sarah Perrin; Tatiana Matejovicova; Alexandre Ramé; Morgane Rivière; Louis Rouillard; Thomas Mesnard; Geoffrey Cideron; et al. (CoRR, 2025)

MedGemma Technical Report. - Andrew Sellergren; Sahar Kazemzadeh; Tiam Jaroensri; Atilla P. Kiraly; Madeleine Traverse; Timo Kohlberger; Shawn Xu; Fayaz Jamil; Cían Hughes; Charles Lau; Justin Chen; Fereshteh Mahvar; et al. (CoRR, 2025)