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Preference-PCA Pluralistic Learning (P3L): Steerable Pluralistic Policy Alignment from Anonymous Crowd Feedback

The P3L (Preference-PCA Pluralistic Learning) framework jointly learns trajectory embeddings and a shared basis of preference directions via PCA decomposition from anonymous, heterogeneous crowd feedback, enabling personalized reward learning and downstream policy optimization without requiring annotator identities. In experiments across multiple embodied control tasks, P3L supports steerable personal alignment, generalizes beyond the offline dataset to out-of-distribution new users, and achieves competitive reinforcement learning and test performance while substantially reducing computational cost and supporting variable adaptation query budgets.

Part of the event: Proposing the P3L Pluralistic Alignment Framework under Anonymous Crowdsourced Feedback

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