Diffusion Policy Optimization without Drifting Apart

In three linesDiPOD, a diffusion policy optimization method, addresses RL post-training instability by identifying double-drift phenomenon (ELBO diverging from true log-likelihood). The approach interleaves self-distillation with policy-improving gradient updates, stabilizing training on language models and continuous-control tasks.

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