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Steerable Pluralistic Policy Alignment from Anonymized Crowd Feedback
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pluralistic alignmentreinforcement learningpersonalization
2610.26914
Builder Relevance
2h ago80%
Abstract
The paper presents a novel framework for pluralistic alignment that learns from diverse crowd feedback without requiring annotator identities.
Reality Card
Core Claim
The proposed Preference-PCA Pluralistic Learning (P3L) framework enables rapid and accurate personalized reward learning and downstream policy optimization while generalizing to new users.
Method / Result
P3L reduces computational cost and supports variable adaptation query budgets.
Limitations
The reliance on heterogeneous crowd feedback may introduce variability that affects reproducibility.
Paper to code
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