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Contrastive Preference Learning: Learning from Human Feedback without RL
This paper introduces Contrastive Preference Learning (CPL), a new algorithm for optimizing behavior from human feedback without learning reward functions. CPL is fully off-policy and can be applied to arbitrary MDPs, making it scalable and simpler than prior methods.
https://arxiv.org/abs//2310.13639
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
This paper introduces Contrastive Preference Learning (CPL), a new algorithm for optimizing behavior from human feedback without learning reward functions. CPL is fully off-policy and can be applied to arbitrary MDPs, making it scalable and simpler than prior methods.
https://arxiv.org/abs//2310.13639
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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