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This research addresses critical limitations in Denoising Diffusion Policy Optimization (DDPO), specifically the trade-off between reward alignment and output diversity. By challenging previous assumptions about timestep updates and introducing incremental Feynman-Kac training, the authors offer a theoretically grounded method to enhance both metrics simultaneously. The findings provide significant value for practitioners developing diffusion models who require robust strategies to prevent mode collapse while optimizing performance.
iADD: Improving Alignment and Diversity in Diffusion Policy Optimization
arXiv:2610.01789v1 Announce Type: cross Abstract: Reinforcement learning based post training of diffusion models, such as Denoising Diffusion Policy Optimization (DDPO), optimizes a reverse diffusion process under a reward function. However, current approaches to reward optimizations do so at the cost of diversity and quality. In this paper, we provide better tradeoffs through careful theoretical considerations and method design. We analyze the theoretical framework and mathematically demonstrate that \emph{only-latter timestep} updates of diffusion model may be harmful for diversity contrary to the conclusions presented in a previous work. Additionally, we propose an incremental Feynman-Kac training based on strong theoretical foundations in order to achieve the best-yet alignment-diversity tradeoffs. We perform extensive experiments and compare our method against related diffusion policy optimization approaches in three different tasks and also provide strong ablations for each component, thus validating strong performance gains in both alignment and diversity.
Abstract:Reinforcement learning based post training of diffusion models, such as Denoising Diffusion Policy Optimization (DDPO), optimizes a reverse diffusion process under a reward function. However, current approaches to reward optimizations do so at the cost of diversity and quality. In this paper, we provide better tradeoffs through careful theoretical considerations and method design. We analyze the theoretical framework and mathematically demonstrate that \emph{only-latter timestep} updates of diffusion model may be harmful for diversity contrary to the conclusions presented in a previous work. Additionally, we propose an incremental Feynman-Kac training based on strong theoretical foundations in order to achieve the best-yet alignment-diversity tradeoffs. We perform extensive experiments and compare our method against related diffusion policy optimization approaches in three different tasks and also provide strong ablations for each component, thus validating strong performance gains in both alignment and diversity.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2610.01789 [cs.LG]
(or arXiv:2610.01789v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.01789 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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