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arXiv · 2409.08400

Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning

Hanyang Zhao·Haoxian Chen·Ji Zhang·David D. Yao·Wenpin Tang
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Abstract

Reinforcement Learning from human feedback (RLHF) has been shown a promising direction for aligning generative models with human intent and has also been explored in recent works for alignment of diffusion generative models. In this work, we provide a rigorous treatment by formulating the task of fine-tuning diffusion models, with reward functions learned from human feedback, as an exploratory continuous-time stochastic control problem. Our key idea lies in treating the score-matching functions as controls/actions, and upon this, we develop a unified framework from a continuous-time perspective, to employ reinforcement learning (RL) algorithms in terms of improving the generation quality of diffusion models. We also develop the corresponding continuous-time RL theory for policy optimization and regularization under assumptions of stochastic different equations driven environment. Experiments on the text-to-image (T2I) generation will be reported in the accompanied paper.

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Hanyang Zhao, Haoxian Chen, Ji Zhang, David D. Yao, Wenpin Tang. 2024-09-12. Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning. https://arxiv.org/abs/2409.08400

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