Geometric deep reinforcement learning for dynamic DAG scheduling. - Nathan Grinsztajn; Olivier Beaumont; Emmanuel Jeannot; Philippe Preux (SSCI, 2020)

READYS: A Reinforcement Learning Based Strategy for Heterogeneous Dynamic Scheduling. - Nathan Grinsztajn; Olivier Beaumont; Emmanuel Jeannot; Philippe Preux (CLUSTER, 2021)

There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning. - Nathan Grinsztajn; Johan Ferret; Olivier Pietquin; Philippe Preux; Matthieu Geist (NeurIPS, 2021)

MetaREVEAL: RL-based Meta-learning from Learning Curves. - Manh Hung Nguyen; Isabelle Guyon; Lisheng Sun-Hosoya; Nathan Grinsztajn (IAL@PKDD/ECML, 2021)

Interferometric Graph Transform for Community Labeling. - Nathan Grinsztajn; Louis Leconte; Philippe Preux; Edouard Oyallon (CoRR, 2021)

Low-Rank Projections of GCNs Laplacian. - Nathan Grinsztajn; Philippe Preux; Edouard Oyallon (CoRR, 2021)

More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences. - Toby Johnstone; Nathan Grinsztajn; Johan Ferret; Philippe Preux (CoRR, 2021)

Meta-learning from Learning Curves: Challenge Design and Baseline Results. - Manh Hung Nguyen; Lisheng Sun-Hosoya; Nathan Grinsztajn; Isabelle Guyon (IJCNN, 2022)

Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round. - Manh Hung Nguyen; Lisheng Sun; Nathan Grinsztajn; Isabelle Guyon (CoRR, 2022)

Population-Based Reinforcement Learning for Combinatorial Optimization. - Nathan Grinsztajn; Daniel Furelos-Blanco; Thomas D. Barrett (CoRR, 2022)

Combinatorial Optimization with Policy Adaptation using Latent Space Search. - Félix Chalumeau; Shikha Surana; Clément Bonnet; Nathan Grinsztajn; Arnu Pretorius; Alexandre Laterre; Tom Barrett (NeurIPS, 2023)

Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization. - Nathan Grinsztajn; Daniel Furelos-Blanco; Shikha Surana; Clément Bonnet; Tom Barrett (NeurIPS, 2023)

Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX. - Clément Bonnet; Daniel Luo; Donal Byrne; Shikha Surana; Vincent Coyette; Paul Duckworth; Laurence I. Midgley; Tristan Kalloniatis; Sasha Abramowitz; Cemlyn N. Waters; Andries P. Smit; Nathan Grinsztajn; et al. (CoRR, 2023)

Combinatorial Optimization with Policy Adaptation using Latent Space Search. - Félix Chalumeau; Shikha Surana; Clément Bonnet; Nathan Grinsztajn; Arnu Pretorius; Alexandre Laterre; Thomas D. Barrett (CoRR, 2023)

Are we going MAD? Benchmarking Multi-Agent Debate between Language Models for Medical Q&A. - Andries P. Smit; Paul Duckworth; Nathan Grinsztajn; Kale-ab Abebe Tessera; Thomas D. Barrett; Arnu Pretorius (CoRR, 2023)

Reinforcement learning for combinatorial optimization : leveraging uncertainty, structure and priors. (Apprentissage par renforcement pour l'optimisation combinatoire : exploiter l'incertitude, les structures et les connaissances a priori). - Nathan Grinsztajn (2023)

Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion. - Yannis Flet-Berliac; Nathan Grinsztajn; Florian Strub; Eugene Choi; Bill Wu; Chris Cremer; Arash Ahmadian; Yash Chandak; Mohammad Gheshlaghi Azar; Olivier Pietquin; Matthieu Geist (EMNLP, 2024)

Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX. - Clément Bonnet; Daniel Luo; Donal Byrne; Shikha Surana; Sasha Abramowitz; Paul Duckworth; Vincent Coyette; Laurence Illing Midgley; Elshadai Tegegn; Tristan Kalloniatis; Omayma Mahjoub; Matthew Macfarlane; et al. (ICLR, 2024)

Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs. - Andries P. Smit; Nathan Grinsztajn; Paul Duckworth; Thomas D. Barrett; Arnu Pretorius (ICML, 2024)

Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial Optimization. - Félix Chalumeau; Refiloe Shabe; Noah de Nicola; Arnu Pretorius; Thomas D. Barrett; Nathan Grinsztajn (CoRR, 2024)

Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion. - Yannis Flet-Berliac; Nathan Grinsztajn; Florian Strub; Eugene Choi; Chris Cremer; Arash Ahmadian; Yash Chandak; Mohammad Gheshlaghi Azar; Olivier Pietquin; Matthieu Geist (CoRR, 2024)

Averaging log-likelihoods in direct alignment. - Nathan Grinsztajn; Yannis Flet-Berliac; Mohammad Gheshlaghi Azar; Florian Strub; Bill Wu; Eugene Choi; Chris Cremer; Arash Ahmadian; Yash Chandak; Olivier Pietquin; Matthieu Geist (CoRR, 2024)

Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier. - John Dang; Shivalika Singh; Daniel D'souza; Arash Ahmadian; Alejandro Salamanca; Madeline Smith; Aidan Peppin; Sungjin Hong; Manoj Govindassamy; Terrence Zhao; Sandra Kublik; Meor Amer; et al. (CoRR, 2024)

Overconfident Oracles: Limitations of In Silico Sequence Design Benchmarking. - Shikha Surana; Nathan Grinsztajn; Timothy Atkinson 003; Paul Duckworth; Thomas D. Barrett (CoRR, 2025)

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)