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

arXiv cs.RO / 5/4/2026

📰 NewsModels & Research

共有:

Key Points

PrefMoE introduces a mixture-of-experts approach for preference-based reinforcement learning, aiming to improve robustness when preference data is noisy, heterogeneous, or partially conflicting.
Instead of fitting one reward model to all comparative feedback, it learns multiple specialized reward “experts” and combines them with trajectory-level soft routing to capture different latent preference patterns.
A load-balancing regularizer is used to stabilize training and prevent expert collapse, helping the ensemble remain diverse and effective.
Experiments on D4RL locomotion benchmarks and MetaWorld manipulation tasks show that PrefMoE improves preference prediction robustness and yields more reliable downstream policy learning than strong single-reward-model baselines.

Abstract

Preference-based reinforcement learning offers a scalable alternative to manual reward engineering by learning reward structures from comparative feedback. However, large-scale preference datasets, whether collected from crowdsourced annotators or generated by synthetic teachers, often contain heterogeneous and partially conflicting supervision, including disagreement across annotators and inconsistency within annotators. Existing reward learning methods typically fit a single reward model to such data, forcing it to average incompatible signals and thereby limiting robustness. To solve this, we propose PrefMoE, a mixture-of-experts reward learning framework for robust preference modeling. PrefMoE learns multiple specialized reward experts and uses trajectory-level soft routing to combine them adaptively, enabling the model to capture diverse latent preference patterns under noisy and heterogeneous preference supervision. A load-balancing regularizer further stabilizes training by preventing expert collapse. Across locomotion benchmarks from D4RL and manipulation tasks from MetaWorld, PrefMoE improves preference prediction robustness and leads to more reliable downstream policy learning than strong single-model baselines.