570 citations · 2.6k across the 75 of their papers we have counts for
82 papers
Bench-MFG: A Benchmark Suite for Learning in Stationary Mean Field Games
Lorenzo Magnino, Jiacheng Shen, Matthieu Geist +2
The intersection of Mean Field Games (MFGs) and Reinforcement Learning (RL) has fostered a growing family of algorithms designed to solve large-scale multi-agent systems. However,…
Population-aware Online Mirror Descent for Mean-Field Games with Common Noise by Deep Reinforcement Learning
Zida Wu, Mathieu Lauriere, Matthieu Geist +2
Mean Field Games (MFGs) offer a powerful framework for studying large-scale multi-agent systems. Yet, learning Nash equilibria in MFGs remains a challenging problem, particularly w…
ShiQ: Bringing back Bellman to LLMs
Pierre Clavier, Nathan Grinsztajn, Raphael Avalos +8
The fine-tuning of pre-trained large language models (LLMs) using reinforcement learning (RL) is generally formulated as direct policy optimization. This approach was naturally fav…
Averaging log-likelihoods in direct alignment
Nathan Grinsztajn, Yannis Flet-Berliac, Mohammad Gheshlaghi Azar +8
To better align Large Language Models (LLMs) with human judgment, Reinforcement Learning from Human Feedback (RLHF) learns a reward model and then optimizes it using regularized RL…
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion
Yannis Flet-Berliac, Nathan Grinsztajn, Florian Strub +8
Reinforcement Learning (RL) has been used to finetune Large Language Models (LLMs) using a reward model trained from preference data, to better align with human judgment. The recen…
Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Zida Wu, Mathieu Lauriere, Samuel Jia Cong Chua +3
Mean Field Games (MFGs) have the ability to handle large-scale multi-agent systems, but learning Nash equilibria in MFGs remains a challenging task. In this paper, we propose a dee…
MusicRL: Aligning Music Generation to Human Preferences
Geoffrey Cideron, Sertan Girgin, Mauro Verzetti +11
We propose MusicRL, the first music generation system finetuned from human feedback. Appreciation of text-to-music models is particularly subjective since the concept of musicality…
Learning Discrete-Time Major-Minor Mean Field Games
Kai Cui, Gökçe Dayanıklı, Mathieu Laurière +3
Recent techniques based on Mean Field Games (MFGs) allow the scalable analysis of multi-player games with many similar, rational agents. However, standard MFGs remain limited to ho…
On Imitation in Mean-field Games
Giorgia Ramponi, Pavel Kolev, Olivier Pietquin +3
We explore the problem of imitation learning (IL) in the context of mean-field games (MFGs), where the goal is to imitate the behavior of a population of agents following a Nash eq…
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Paul Roit, Johan Ferret, Lior Shani +16
Despite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input. This phenomenon i…
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang +12
Mirror descent value iteration (MDVI), an abstraction of Kullback-Leibler (KL) and entropy-regularized reinforcement learning (RL), has served as the basis for recent high-performi…
Get Back Here: Robust Imitation by Return-to-Distribution Planning
Geoffrey Cideron, Baruch Tabanpour, Sebastian Curi +6
We consider the Imitation Learning (IL) setup where expert data are not collected on the actual deployment environment but on a different version. To address the resulting distribu…
Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision
Eugene Kharitonov, Damien Vincent, Zalán Borsos +6
We introduce SPEAR-TTS, a multi-speaker text-to-speech (TTS) system that can be trained with minimal supervision. By combining two types of discrete speech representations, we cast…
SingSong: Generating musical accompaniments from singing
Chris Donahue, Antoine Caillon, Adam Roberts +8
We present SingSong, a system that generates instrumental music to accompany input vocals, potentially offering musicians and non-musicians alike an intuitive new way to create mus…
On the importance of data collection for training general goal-reaching policies
Alexis Jacq, Manu Orsini, Gabriel Dulac-Arnold +3
Recent advances in ML suggest that the quantity of data available to a model is one of the primary bottlenecks to high performance. Although for language-based tasks there exist al…
Emergent Communication: Generalization and Overfitting in Lewis Games
Mathieu Rita, Corentin Tallec, Paul Michel +4
Lewis signaling games are a class of simple communication games for simulating the emergence of language. In these games, two agents must agree on a communication protocol in order…
vec2text with Round-Trip Translations
Geoffrey Cideron, Sertan Girgin, Anton Raichuk +3
We investigate models that can generate arbitrary natural language text (e.g. all English sentences) from a bounded, convex and well-behaved control space. We call them universal v…
AudioLM: a Language Modeling Approach to Audio Generation
Zalán Borsos, Raphaël Marinier, Damien Vincent +8
We introduce AudioLM, a framework for high-quality audio generation with long-term consistency. AudioLM maps the input audio to a sequence of discrete tokens and casts audio genera…
Learning Correlated Equilibria in Mean-Field Games
Paul Muller, Romuald Elie, Mark Rowland +7
The designs of many large-scale systems today, from traffic routing environments to smart grids, rely on game-theoretic equilibrium concepts. However, as the size of an -player…
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal
Tadashi Kozuno, Wenhao Yang, Nino Vieillard +10
In this work, we consider and analyze the sample complexity of model-free reinforcement learning with a generative model. Particularly, we analyze mirror descent value iteration (M…
Learning in Mean Field Games: A Survey
Mathieu Laurière, Sarah Perrin, Julien Pérolat +5
Non-cooperative and cooperative games with a very large number of players have many applications but remain generally intractable when the number of players increases. Introduced b…
On the role of population heterogeneity in emergent communication
Mathieu Rita, Florian Strub, Jean-Bastien Grill +2
Populations have often been perceived as a structuring component for language to emerge and evolve: the larger the population, the more structured the language. While this observat…
Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act
Alexis Jacq, Johan Ferret, Olivier Pietquin +1
Traditionally, Reinforcement Learning (RL) aims at deciding how to act optimally for an artificial agent. We argue that deciding when to act is equally important. As humans, we dri…
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
Mathieu Laurière, Sarah Perrin, Sertan Girgin +8
Mean Field Games (MFGs) have been introduced to efficiently approximate games with very large populations of strategic agents. Recently, the question of learning equilibria in MFGs…