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October 3, 2025Open Access

DiffusionNFT: Online Diffusion Reinforcement with Forward Process

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Novel RL method optimizes diffusion models using flow matching and enhances performance without CFG.

DiffusionNFT improves efficiency in training diffusion models through direct forward process optimization, achieving a substantial reduction in resource usage.
With a GenEval score increase from 0.24 to 0.98 in 1k steps, DiffusionNFT outperforms FlowGRPO which requires more than 5k steps.
This approach incorporates multiple reward models to enhance overall performance across various benchmarks on diffusion tasks.
Eliminating the need for likelihood estimation allows for cleaner images and simpler integration, leading to a more effective RL paradigm.

Zheng et al. (2025) studied this question.

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