arXiv Machine Learning June 2, 2026 By Jun-Jie Yang, Chia-Heng Hsu, Kui-Yuan Chen, Ping-Chun Hsieh

From Reward-Free Representations to Preferences: Rethinking Offline Preference-Based Reinforcement Learning

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arXiv:2606. 01123v1 Announce Type: new Abstract: Preference-based reinforcement learning (PbRL) avoids explicit reward engineering by learning from pairwise human preference feedback.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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