PrefMoE: Robust Preference Modeling with Mixture-of-Experts Reward Learning

arXiv:2605.00384 · cs.RO · Submitted 2026-05-01 · Read on arXiv

cs.RO

Submitted: 2026-05-01

Updated: 2026-09-26

Project page: https://sites.google.com/view/moepref

Terminology

• Reinforcement Learning — 18× in this paper · explained in 4 episodes
• Transformer — 13× in this paper · explained in 2 episodes
• Robustness — 12× in this paper · explained in 5 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Implicit Q-Learning — 2× in this paper
• International Conference on Learning Representations — 2× in this paper
• Q-learning — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Decoupling — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes

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