Diffusion models are a class of flexible generative models trained with an approximation to the log-likelihood objective. However, most use cases of diffusion models are not concerned with likelihoods, but instead with downstream objectives such as human-perceived image quality or drug effectiveness. In this paper, we investigate reinforcement learning methods for directly optimizing diffusion models for such objectives. We describe how posing denoising as a multi-step decision-making problem enables a class of policy gradient algorithms, which we refer to as denoising diffusion policy optimization (DDPO), that are more effective than alternative reward-weighted likelihood approaches. Empirically, DDPO is able to adapt text-to-image diffusion models to objectives that are difficult to express via prompting, such as image compressibility, and those derived from human feedback, such as aesthetic quality. Finally, we show that DDPO can improve prompt-image alignment using feedback from a vision-language model without the need for additional data collection or human annotation. The project's website can be found at http://rl-diffusion.github.io .

翻译：扩散模型是一类通过近似对数似然目标训练得到的灵活生成模型。然而，扩散模型的大多数实际应用并不关注似然值，而是关注下游目标，例如人类感知的图像质量或药物有效性。本文研究了直接针对此类目标优化扩散模型的强化学习方法。我们描述了如何将去噪过程建模为多步决策问题，从而使得一类策略梯度算法——我们称之为去噪扩散策略优化（DDPO）——能够比替代的奖励加权似然方法更有效。实验结果显示，DDPO能够使文本到图像扩散模型适应难以通过提示表达的目标（如图像可压缩性）以及源于人类反馈的目标（如美学质量）。最后，我们证明，DDPO可利用视觉语言模型的反馈改善提示-图像对齐效果，而无需额外数据收集或人工标注。项目网站地址为：http://rl-diffusion.github.io。