3 papers
cs.LG2026
Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment
Jialu Wang, Heinrich Peters, Asad A. Butt +6
Despite their sophisticated general-purpose capabilities, Large Language Models (LLMs) often fail to align with diverse individual preferences because standard post-training method…
cs.LG2025
Iterative Foundation Model Fine-Tuning on Multiple Rewards
Pouya M. Ghari, Simone Sciabola, Ye Wang
Fine-tuning foundation models has emerged as a powerful approach for generating objects with specific desired properties. Reinforcement learning (RL) provides an effective framewor…
cs.LG2024
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning
Pouya M. Ghari, Yanning Shen
Federated learning is renowned for its efficacy in distributed model training, ensuring that users, called clients, retain data privacy by not disclosing their data to the central…