Bonjour à tous,

Vous êtes tous et toutes cordialement invité.es à assister à la présentation de projet du prédoc III de Liu Zichu, le 29 août à 14h (mode hybride).

Titre : Surrogate Methods for Solving Non-Monotone Variational Inequalities in Reinforcement Learning

Date: vendredi 29 août à 14h.

Location: Espace de cotravail, Mila 6650 (1er étage)

Jury

Résumé

World modeling aims to build robust AI systems that can simulate reality, a goal that fundamentally requires generative diversity to prevent narrow,brittle worldviews. My research investigates this critical need fordiversity, using text-to-image generation as a practical case study. We propose a benchmarking framework to evaluate the utility of synthetic datagenerated by text-to-image (T2I) models, comparing the aesthetic quality,diversity and consistency between generated contents and real data, and highlighting prompt complexity as a key factor for enhancing diversity. Wealso focus on bias analysis in unconditional image generation, showing thatfeature probability shifts between training and generation are often smaller than assumed, and that classifier-based evaluations can alignclosely with human perception of bias. Building on this, my future workwill extend the study of diversity from generation to reasoning within multimodal models and reinforcement learning, showcasing that diversity isan important factor to build next generation models.