Targeted Attacks on Deep Reinforcement Learning Agents through Adversarial Observations. - Léonard Hussenot; Matthieu Geist; Olivier Pietquin (CoRR, 2019)

CopyCAT: : Taking Control of Neural Policies with Constant Attacks. - Léonard Hussenot; Matthieu Geist; Olivier Pietquin (AAMAS, 2020)

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)

Show Me the Way: Intrinsic Motivation from Demonstrations. - Léonard Hussenot; Robert Dadashi; Matthieu Geist; Olivier Pietquin (AAMAS, 2021)

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)

Primal Wasserstein Imitation Learning. - Robert Dadashi; Léonard Hussenot; Matthieu Geist; Olivier Pietquin (ICLR, 2021)

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

RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning. - Sabela Ramos; Sertan Girgin; Léonard Hussenot; Damien Vincent; Hanna Yakubovich; Daniel Toyama; Anita Gergely; Piotr Stanczyk; Raphaël Marinier; Jeremiah Harmsen; Olivier Pietquin; Nikola Momchev (CoRR, 2021)

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

Continuous Control with Action Quantization from Demonstrations. - Robert Dadashi; Léonard Hussenot; Damien Vincent; Sertan Girgin; Anton Raichuk; Matthieu Geist; Olivier Pietquin (ICML, 2022)

Learning Energy Networks with Generalized Fenchel-Young Losses. - Mathieu Blondel; Felipe Llinares-López; Robert Dadashi; Léonard Hussenot; Matthieu Geist (NeurIPS, 2022)

vec2text with Round-Trip Translations. - Geoffrey Cideron; Sertan Girgin; Anton Raichuk; Olivier Pietquin; Olivier Bachem; Léonard Hussenot (CoRR, 2022)

Apprentissage par démonstrations : transfert des motivations humaines aux algorithmes. (Apprenticeship learning : transferring human motivations to artificial agents). - Léonard Hussenot (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)

Get Back Here: Robust Imitation by Return-to-Distribution Planning. - Geoffrey Cideron; Baruch Tabanpour; Sebastian Curi; Sertan Girgin; Léonard Hussenot; Gabriel Dulac-Arnold; Matthieu Geist; Olivier Pietquin; Robert Dadashi (CoRR, 2023)

MusicRL: Aligning Music Generation to Human Preferences. - Geoffrey Cideron; Sertan Girgin; Mauro Verzetti; Damien Vincent; Matej Kastelic; Zalán Borsos; Brian McWilliams; Victor Ungureanu; Olivier Bachem; Olivier Pietquin; Matthieu Geist; Léonard Hussenot; et al. (ICML, 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)

Gemma: Open Models Based on Gemini Research and Technology. - Thomas Mesnard; Cassidy Hardin; Robert Dadashi; Surya Bhupatiraju; Shreya Pathak; Laurent Sifre; Morgane Rivière; Mihir Sanjay Kale; Juliette Love; Pouya Tafti; Léonard Hussenot; Aakanksha Chowdhery; et al. (CoRR, 2024)

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models. - Aleksandar Botev; Soham De; Samuel L. Smith; Anushan Fernando; George-Cristian Muraru; Ruba Haroun; Leonard Berrada; Razvan Pascanu; Pier Giuseppe Sessa; Robert Dadashi; Léonard Hussenot; Johan Ferret; et al. (CoRR, 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)

Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning. - Kaiwen Wang; Rahul Kidambi; Ryan Sullivan; Alekh Agarwal; Christoph Dann; Andrea Michi; Marco Gelmi; Yunxuan Li; Raghav Gupta; Avinava Dubey; Alexandre Ramé; Johan Ferret; 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)