Exploration and Communication for Partially Observable Collaborative Multi-Agent Reinforcement Learning. - Raphaël Avalos (AAMAS, 2022)

Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning. - Raphaël Avalos; Mathieu Reymond; Ann Nowé; Diederik M. Roijers (AAMAS, 2022)

Laser Learning Environment: A New Environment for Coordination-Critical Multi-agent Tasks. - Yannick Molinghen; Raphaël Avalos; Mark Van Achter; Ann Nowé; Tom Lenaerts (BNAIC/BENELEARN, 2023)

Local Advantage Networks for Multi-Agent Reinforcement Learning in Dec-POMDPs. - Raphaël Avalos; Mathieu Reymond; Ann Nowé; Diederik M. Roijers (Trans. Mach. Learn. Res., 2023)

The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models. - Raphaël Avalos; Florent Delgrange; Ann Nowé; Guillermo A. Pérez 001; Diederik M. Roijers (ICLR, 2024)

Online Planning in POMDPs with State-Requests. - Raphaël Avalos; Eugenio Bargiacchi; Ann Nowé; Diederik M. Roijers; Frans A. Oliehoek (RLJ, 2024)

Dynamic Size Message Scheduling for Multi-Agent Communication Under Limited Bandwidth. - Qingshuang Sun; Denis Steckelmacher; Yuan Yao 004; Ann Nowé; Raphaël Avalos (IEEE Trans. Mob. Comput., 2024)

Command A: An Enterprise-Ready Large Language Model. - Aakanksha; Arash Ahmadian; Marwan Ahmed; Jay Alammar; Milad Alizadeh; Yazeed Alnumay; Sophia Althammer; Arkady Arkhangorodsky; Viraat Aryabumi; Dennis Aumiller; Raphaël Avalos; Zahara Aviv; et al. (CoRR, 2025)

ShiQ: Bringing back Bellman to LLMs. - Pierre Clavier; Nathan Grinsztajn; Raphaël Avalos; Yannis Flet-Berliac; Irem Ergün; Omar Darwiche Domingues; Eugene Tarassov; Olivier Pietquin; Pierre H. Richemond; Florian Strub; Matthieu Geist (CoRR, 2025)

Sample, Predict, then Proceed: Self-Verification Sampling for Tool Use of LLMs. - Shangmin Guo; Omar Darwiche Domingues; Raphaël Avalos; Aaron C. Courville; Florian Strub (CoRR, 2025)