DiPOD: Diffusion Policy Optimization without Drifting Apart

• URL: http://arxiv.org/abs/2606.13795v2
• Date: Wed, 17 Jun 2026 17:53:46 GMT
• Title: DiPOD: Diffusion Policy Optimization without Drifting Apart
• Abstract summary: RL post-training has become increasingly pivotal for improving diffusion policies.<n>Existing diffusion policy-gradient methods are often unstable and cannot achieve reliable policy improvement.<n>We propose bftextDiPOD, a diffusion policy optimization framework that maintains tight-bound behavior throughout training.
• Score: 88.2714417395158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 behavior throughout training by interleaving self-distillation with policy-improving gradient updates. This leads to a simple and practical algorithm: augmenting each diffusion policy-gradient update with an on-policy ELBO regularizer. Across diffusion language model post-training and continuous-control diffusion policies, DiPOD substantially stabilizes training and reaches higher rewards than previous methods.

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