Mathieu Rita — accepted AI-conference papers

2026

Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments

Romain Froger, Amine Benhalloum, Andrey Rusakov, Dheeraj Mekala, Emilien Garreau, Gerard Moreno-Torres Bertran +18

ICLR 2026oral

We introduce **Gaia2**, a benchmark for evaluating large language model agents in realistic, asynchronous environments. Unlike prior static or synchronous evaluations, Gaia2 introduces scenarios where environments evolve independently of agent actions, requiring agents to operate under temporal cons…

Cited by 0 Source ↗Scholar ↗

2024

Countering Reward Over-Optimization in LLM with Demonstration-Guided Reinforcement Learning

Mathieu Rita, Florian Strub, Rahma Chaabouni, Paul Michel, Emmanuel Dupoux, Olivier Pietquin

ACL 2024findings

While reinforcement learning (RL) has been proven essential for tuning large language models (LLMs), it can lead to reward over-optimization (ROO). Existing approaches address ROO by adding KL regularization, requiring computationally expensive hyperparameter tuning. Additionally, KL regularization…

Cited by 9 Source ↗PDF ↗code ↗Scholar ↗

2023

On the Correspondence between Compositionality and Imitation in Emergent Neural Communication

Emily Cheng, Mathieu Rita, Thierry Poibeau

ACL 2023findings

Compositionality is a hallmark of human language that not only enables linguistic generalization, but also potentially facilitates acquisition. When simulating language emergence with neural networks, compositionality has been shown to improve communication performance; however, its impact on imitat…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2023

Revisiting Populations in multi-agent Communication

Paul Michel, Mathieu Rita, Kory Wallace Mathewson, Olivier Tieleman, Angeliki Lazaridou

ICLR 2023poster

Despite evidence from cognitive sciences that larger groups of speakers tend to develop more structured languages in human communication, scaling up to populations has failed to yield significant benefits in emergent multi-agent communication. In this paper we advocate for an alternate population-le…

Cited by 10 Source ↗PDF ↗Scholar ↗

2022

Emergent Communication: Generalization and Overfitting in Lewis Games

Mathieu Rita, Corentin Tallec, Paul Michel, Jean-Bastien Grill, Olivier Pietquin, Emmanuel Dupoux +1

NeurIPS 2022accept

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 to solve a cooperative task. Previous work has shown that agents trained to play this game with reinforcement learning…

Cited by 33 Source ↗PDF ↗code ↗Scholar ↗

2022

On the role of population heterogeneity in emergent communication

Mathieu Rita, Florian Strub, Jean-Bastien Grill, Olivier Pietquin, Emmanuel Dupoux

ICLR 2022poster

Populations have often been perceived as a structuring component for language to emerge and evolve: the larger the population, the more systematic the language. While this observation is widespread in the sociolinguistic literature, it has not been reproduced in computer simulations with neural agen…

Cited by 38 Source ↗PDF ↗code ↗Scholar ↗