[Submitted on 17 Oct 2026]
Title:Steerable Pluralistic Policy Alignment from Anonymized Crowd Feedback
View a PDF of the paper titled Steerable Pluralistic Policy Alignment from Anonymized Crowd Feedback, by Akansha Kalra and 3 other authors
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Abstract:Pluralistic alignment requires AI systems to learn from diverse populations whose preferences may reflect heterogeneous or even conflicting objectives, and to personalize their behavior to individual users. Existing approaches in pluralistic alignment often assume access to annotator identities and rely on powerful pretrained representations to model individual preference variations. We propose Preference-PCA Pluralistic Learning (P3L), a novel framework for pluralistic alignment that jointly learns trajectory embeddings and the shared basis of preference directions via PCA decomposition from heterogeneous crowd feedback without requiring annotator identities. At test time, our approach enables rapid and accurate personalized reward learning and downstream policy optimization. Experiments across multiple embodied control tasks demonstrate that P3L enables steerable personal alignment that generalizes beyond the offline dataset to out-of-distribution new users and attains competitive RL and test performance, while substantially reducing computational cost and supporting variable adaptation query budgets.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
Cite as: arXiv:2610.26914 [cs.LG]
(or arXiv:2610.26914v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.26914
arXiv-issued DOI via DataCite
Submission history
From: Akansha Kalra [view email] [v1] Sat, 17 Oct 2026 06:14:59 UTC (2,609 KB)
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