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DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models

Learning from human feedback has been shown to improve text-to-image models. These techniques first learn a reward function that captures what humans care about in the task and then improve the models based on the learned reward function.

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Cite this as

Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh (2024). Dataset: DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models. https://doi.org/10.57702/ltxezq8o

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Additional Info

Field	Value
Created	December 2, 2024
Last update	December 2, 2024
Defined In	https://doi.org/10.48550/arXiv.2305.16381
Author	Ying Fan
More Authors	Olivia Watkins Yuqing Du Hao Liu Moonkyung Ryu Craig Boutilier Pieter Abbeel Mohammad Ghavamzadeh
Homepage	https://github.com/google-research/google-research/tree/master/dpok