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PrefO2O: Zero-Shot Preference Reinforcement Learning via Offline-to-Online Latent Preference Transfer

PrefO2O proposes an offline-to-online latent preference transfer framework that enables zero-shot preference-based reinforcement learning (PbRL) on target tasks without any human feedback. The method learns a task-invariant latent preference space from a small amount of offline preference data on source tasks, disentangles transferable preference structures from task-specific variations, and uses confidence-weighted optimal transport alignment to generate pseudo-preference supervision for online reward adaptation.

Part of the event: PrefO2O: A New Framework for Zero-Shot Preference Reinforcement Learning

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