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PrefO2O: Zero-Shot Preference-Based Reinforcement Learning via Offline-to-Online Latent Preference Transfer — arXiv:2610.31812

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.31812
License (as archived): CC0-1.0
Archived: 2026-10-26T19:51:27.033Z
SHA-256 of the archived text: 8ba796dfda61c4a44008ec68fbf36d8c6c40122205b83063814ba1a3aa43a1e8
Stable URL of this page: https://metraipolis.com/documento/dc_abf5a2b453ea

How to cite

PrefO2O: Zero-Shot Preference-Based Reinforcement Learning via Offline-to-Online Latent Preference Transfer — arXiv:2610.31812 — arXiv (en), https://arxiv.org/abs/2610.31812 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_abf5a2b453ea (Metraipolis archive dc_abf5a2b453ea, sha256 8ba796dfda61c4a44008ec68fbf36d8c6c40122205b83063814ba1a3aa43a1e8)

BibTeX

@misc{dcabf5a2b453ea,
  title = {PrefO2O: Zero-Shot Preference-Based Reinforcement Learning via Offline-to-Online Latent Preference Transfer — arXiv:2610.31812},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.31812},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_abf5a2b453ea; sha256 8ba796dfda61c4a44008ec68fbf36d8c6c40122205b83063814ba1a3aa43a1e8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.31812. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Preference-based reinforcement learning (PbRL) has emerged as a promising paradigm for learning robot behaviors aligned with human intent. However, its scalability is fundamentally limited by the need to collect expensive human preference labels for every new task. In this work, we present PrefO2O, an offline-to-online latent preference transfer framework that enables zero-shot PbRL on unseen target tasks without requiring any human feedback. The core idea is to learn a task-invariant latent preference space from a small amount of offline preference data on source tasks, where transferable preference structures are disentangled from task-specific variations. This learned manifold allows PrefO2O to transfer preference structures and reward priors to new tasks under distribution shift, while confidence-weighted optimal transport alignment generates reliable pseudo-preference supervision for online reward adaptation. Extensive experiments on robotic manipulation and locomotion benchmarks demonstrate that PrefO2O substantially outperforms existing preference-transfer baselines and matches or exceeds PbRL methods trained with real human feedback, while generalizing across multi-to-one and one-to-one transfer settings as well as across different robot embodiments.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.31812 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-26T19:45:33.359Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).