3 papers
cs.AI2026
Beyond Routing Weights: Faithful Response-Level Interpretation of Mixture-of-Experts Reward Models via Contribution Contrast
Yifan Wang, Jinyi Mu, Mayank Jobanputra +4
Reward models are central to learning from human preferences, yet identifying what drives their predictions remains challenging. Recent sparse Mixture-of-Experts (MoE) reward model…
cs.LG2026
Sparse Mixture-of-Experts Reward Models Learn Interpretable and Specialized Experts for Personalized Preference Modeling
Yifan Wang, Jinyi Mu, Mayank Jobanputra +5
Preference modeling plays a central role in reinforcement learning from human feedback (RLHF), enabling large language models (LLMs) to align with human values. However, most exist…
cs.LG2026
Long-term Fairness with Selective Labels
Giovani Valdrighi, Isabel Valera, Marcos Medeiros Raimundo
Long-term fairness algorithms aim to satisfy fairness beyond static and short-term notions by accounting for the dynamics between decision-making policies and population behavior.…