arXiv:2606.13795v1 Announce Type: new Abstract: RL post-training has become increasingly pivotal for improving diffusion policies, but existing diffusion policy-gradient methods are often unstable and cannot achieve reliable policy improvement. We identify the cause as the double-drift phenomenon: optimizing a variational surrogate can let the ELBO separate from the true log-likelihood, which then makes the resulting proxy policy gradient misaligned with the true policy gradient of expected return. We propose \textbf{DiPOD}, a diffusion policy optimization framework that maintains tight-bound
The rapid advancement of AI models necessitates more stable and effective post-training optimization methods to achieve reliable real-world applications.
Improving the stability and effectiveness of policy optimization for diffusion models directly translates to better performance and reliability in AI systems, impacting various applications.
A more reliable framework for optimizing diffusion policies will lead to more robust and higher-performing AI models that were previously unstable or difficult to train effectively.
- · AI researchers and developers
- · Companies utilizing diffusion models (e.g., generative AI, robotics)
- · AI infrastructure providers
- · Competitors with less stable optimization methods
More powerful and consistent generative AI and control systems become deployable in practical settings.
Accelerated development of AI agents that rely on stable policy gradients for learning and adaptation.
Increased adoption of AI technologies across industries due to enhanced reliability and performance.
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Read at arXiv cs.LG