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Baptiste Bonin

baptiste.bonin@mila.quebec
Master's Research - Université Laval
Supervisor
Audrey Durand
Research Topics
GFlowNets
Recommender Systems
Reinforcement Learning

Publications

What are we measuring? A critical examination of MO-MuJoCo and evaluation consistency in continuous multi-objective reinforcement learning
Baptiste Bonin
Caro Strickland
Audrey Durand
Although Multi-Objective Reinforcement Learning (MORL) research relies heavily on MO-MuJoCo as its go-to continuous control benchmark, the v… (see more)alidity of the conclusions drawn from it remain underexamined. In this paper, we first discuss three structural limitations of MO-MuJoCo; 1) its objectives are decomposed from pre-existing scalar rewards rather than independently motivated goals; 2) environments repeat the same underlying trade-off structure across varied locomotion morphologies, providing \textit{surface variety} without genuine \textit{problem diversity}; and 3) empirically approximated Pareto fronts appear broadly convex across research, potentially failing to stress-test the limitations of scalarization-based techniques. Setting these concerns aside, we further demonstrate that algorithmic rankings under MO-MuJoCo are highly sensitive to often undocumented evaluation choices in research papers. Across five evaluation axes, including reference point selection, weight distribution, normalization, return type, and front extraction method, pairwise algorithm rankings reverse in up to 47\% of configurations. Variance decomposition reveals that normalization alone accounts for nearly 69\% of hypervolume variance, suppressing the algorithm impact. Ultimately, we argue that progress in MORL research requires not only increased scrutiny of the benchmarks we rely upon, but also greater clarity in how results obtained within them are reported.
2026-08-14
Finding the Frame @ Reinforcement Learning Conference (published)
openreview.net
openreview.net
LLM-as-a-Judge: Toward World Models for Slate Recommendation Systems
Baptiste Bonin
Maxime Heuillet
Audrey Durand
Modeling user preferences across domains remains a key challenge in slate recommendation (i.e. recommending an ordered sequence of items) re… (see more)search. We investigate how Large Language Models (LLM) can effectively act as world models of user preferences through pairwise reasoning over slates. We conduct an empirical study involving several LLMs on three tasks spanning different datasets. Our results reveal relationships between task performance and properties of the preference function captured by LLMs, hinting towards areas for improvement and highlighting the potential of LLMs as world models in recommender systems.
2025-11-05
ArXiv (preprint)
doi.org
arxiv.org