Boosting Model Performance through Differentially Private Model Aggregation. - Sophia Collet; Robert Dadashi; Zahi N. Karam; Chang Liu; Parinaz Sobhani; Yevgeniy Vahlis; Jichao Zhang (CoRR, 2018)

The Value Function Polytope in Reinforcement Learning. - Robert Dadashi; Marc G. Bellemare; Adrien Ali Taïga; Nicolas Le Roux; Dale Schuurmans (ICML, 2019)

Statistics and Samples in Distributional Reinforcement Learning. - Mark Rowland 001; Robert Dadashi; Saurabh Kumar 004; Rémi Munos; Marc G. Bellemare; Will Dabney (ICML, 2019)

A Geometric Perspective on Optimal Representations for Reinforcement Learning. - Marc G. Bellemare; Will Dabney; Robert Dadashi; Adrien Ali Taïga; Pablo Samuel Castro; Nicolas Le Roux; Dale Schuurmans; Tor Lattimore; Clare Lyle (NeurIPS, 2019)

The Value-Improvement Path: Towards Better Representations for Reinforcement Learning. - Will Dabney; André Barreto 001; Mark Rowland 001; Robert Dadashi; John Quan; Marc G. Bellemare; David Silver (AAAI, 2021)

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

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)

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)

Offline Reinforcement Learning with On-Policy Q-Function Regularization. - Laixi Shi; Robert Dadashi; Yuejie Chi; Pablo Samuel Castro; Matthieu Geist (ECML/PKDD (4), 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)

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)

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)

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)