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Jean-Baptiste Gaya

4 accepted 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 0Source ↗Scholar ↗
2023

Building a Subspace of Policies for Scalable Continual Learning

Jean-Baptiste Gaya, Thang Doan, Lucas Caccia, Laure Soulier, Ludovic Denoyer, Roberta Raileanu
ICLR 2023top-25%

The ability to continuously acquire new knowledge and skills is crucial for autonomous agents. Existing methods are typically based on either fixed-size models that struggle to learn a large number of diverse behaviors, or growing-size models that scale poorly with the number of tasks. In this work,…

Cited by 39Source ↗PDF ↗code ↗Scholar ↗
2023

Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards

Alexandre Rame, Guillaume Couairon, Corentin Dancette, Jean-Baptiste Gaya, Mustafa Shukor, Laure Soulier +1
NeurIPS 2023poster

Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further align the network with the intended usage. Yet the imperfections in the proxy reward may hinder the training and lead to…

Cited by 134Source ↗PDF ↗code ↗Scholar ↗
2022

Learning a subspace of policies for online adaptation in Reinforcement Learning

Jean-Baptiste Gaya, Laure Soulier, Ludovic Denoyer
ICLR 2022poster

Deep Reinforcement Learning (RL) is mainly studied in a setting where the training and the testing environments are similar. But in many practical applications, these environments may differ. For instance, in control systems, the robot(s) on which a policy is learned might differ from the robot(s) o…

Cited by 19Source ↗PDF ↗code ↗Scholar ↗