DIDR aligns one-step diffusion generators by propagating reward-tilted distributions across noise levels, achieving better preference alignment than multi-step teachers in a single step.
The two methods differ only in饾憼target: Method Reward location DI++ endpoint饾懃 0 only Didrevery noise level via DRS Evaluation
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Diff-Instruct with Diffused Reward: Towards Principled One-step Generator RL
DIDR aligns one-step diffusion generators by propagating reward-tilted distributions across noise levels, achieving better preference alignment than multi-step teachers in a single step.