Nino Vieillard

33 papers A* 12A 2Journal 18Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	BOND: Aligning LLMs with Best-of-N Distillation. ICLR 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, Andrea Michi, Danila Sinopalnikov, Sabela Ramos Garea, Amélie Héliou, Aliaksei Severyn, Matthew Hoffman, Nikola Momchev, Olivier Bachem
2025	A*	`conf`	Loss Functions and Operators Generated by f-Divergences. ICML Vincent Roulet, Tianlin Liu, Nino Vieillard, Michael Eli Sander, Mathieu Blondel
2025	J	`jnl`	Loss Functions and Operators Generated by f-Divergences. CoRR Vincent Roulet, Tianlin Liu, Nino Vieillard, Michael E. Sander, Mathieu Blondel
2025	A*	`conf`	On Teacher Hacking in Language Model Distillation. ICML Daniil Tiapkin, Daniele Calandriello, Johan Ferret, Sarah Perrin, Nino Vieillard, Alexandre Ramé, Mathieu Blondel
2025	J	`jnl`	On Teacher Hacking in Language Model Distillation. CoRR Daniil Tiapkin, Daniele Calandriello, Johan Ferret, Sarah Perrin, Nino Vieillard, Alexandre Ramé, Mathieu Blondel
2024	J	`jnl`	BOND: Aligning LLMs with Best-of-N Distillation. CoRR 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, Andrea Michi, Danila Sinopalnikov, Sabela Ramos, Amélie Héliou, Aliaksei Severyn, Matt Hoffman, Nikola Momchev, Olivier Bachem
2024	A*	`conf`	Imitating Language via Scalable Inverse Reinforcement Learning. NeurIPS Markus Wulfmeier, Michael Bloesch, Nino Vieillard, Arun Ahuja, Jörg Bornschein, Sandy H. Huang, Artem Sokolov, Matt Barnes, Guillaume Desjardins, Alex Bewley, Sarah Bechtle, Jost Tobias Springenberg, Nikola Momchev, Olivier Bachem, Matthieu Geist, Martin A. Riedmiller
2024	J	`jnl`	Imitating Language via Scalable Inverse Reinforcement Learning. CoRR Markus Wulfmeier, Michael Bloesch, Nino Vieillard, Arun Ahuja, Jörg Bornschein, Sandy H. Huang, Artem Sokolov, Matt Barnes, Guillaume Desjardins, Alex Bewley, Sarah Maria Elisabeth Bechtle, Jost Tobias Springenberg, Nikola Momchev, Olivier Bachem, Matthieu Geist, Martin A. Riedmiller
2024	A*	`conf`	On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes. ICLR Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, Olivier Bachem
2024	A*	`conf`	WARM: On the Benefits of Weight Averaged Reward Models. ICML Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, Johan Ferret
2024	J	`jnl`	WARM: On the Benefits of Weight Averaged Reward Models. CoRR Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, Johan Ferret
2024	J	`jnl`	WARP: On the Benefits of Weight Averaged Rewarded Policies. CoRR Alexandre Ramé, Johan Ferret, Nino Vieillard, Robert Dadashi, Léonard Hussenot, Pierre-Louis Cedoz, Pier Giuseppe Sessa, Sertan Girgin, Arthur Douillard, Olivier Bachem
2023	—	`conf`	Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. ACL (1) 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, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor
2023	J	`jnl`	Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. CoRR Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Léonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor
2023	J	`jnl`	GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models. CoRR Rishabh Agarwal, Nino Vieillard, Piotr Stanczyk, Sabela Ramos, Matthieu Geist, Olivier Bachem
2023	A*	`conf`	Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. ICML 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, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo
2023	J	`jnl`	Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. CoRR 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, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo
2022	A	`conf`	Implicitly Regularized RL with Implicit Q-values. AISTATS Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist
2022	J	`jnl`	KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. CoRR 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, Csaba Szepesvári
2022	A*	`conf`	Offline Reinforcement Learning as Anti-exploration. AAAI Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist
2021	J	`jnl`	Implicitly Regularized RL with Implicit Q-Values. CoRR Nino Vieillard, Marcin Andrychowicz, Anton Raichuk, Olivier Pietquin, Matthieu Geist
2021	J	`jnl`	Offline Reinforcement Learning as Anti-Exploration. CoRR Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist
2021	A*	`conf`	Offline Reinforcement Learning with Pseudometric Learning. ICML Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist
2021	J	`jnl`	Offline Reinforcement Learning with Pseudometric Learning. CoRR Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist
2020	A*	`conf`	Deep Conservative Policy Iteration. AAAI Nino Vieillard, Olivier Pietquin, Matthieu Geist
2020	A*	`conf`	Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning. NeurIPS Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist
2020	J	`jnl`	Leverage the Average: an Analysis of Regularization in RL. CoRR Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist
2020	A	`conf`	Momentum in Reinforcement Learning. AISTATS Nino Vieillard, Bruno Scherrer, Olivier Pietquin, Matthieu Geist
2020	A*	`conf`	Munchausen Reinforcement Learning. NeurIPS Nino Vieillard, Olivier Pietquin, Matthieu Geist
2020	J	`jnl`	Munchausen Reinforcement Learning. CoRR Nino Vieillard, Olivier Pietquin, Matthieu Geist
2019	J	`jnl`	Deep Conservative Policy Iteration. CoRR Nino Vieillard, Olivier Pietquin, Matthieu Geist
2019	J	`jnl`	Momentum in Reinforcement Learning. CoRR Nino Vieillard, Bruno Scherrer, Olivier Pietquin, Matthieu Geist
2019	J	`jnl`	On Connections between Constrained Optimization and Reinforcement Learning. CoRR Nino Vieillard, Olivier Pietquin, Matthieu Geist