Diff-Instruct with Diffused Reward: Towards Principled One-step Generator RL
Recent advances in one-step text-to-image generation have enabled real-time synthesis with remarkable efficiency and quality. Previous reinforcement learning methods for one-step generators combine image-space reward optimization with diffusion noisy-space distribution matching. This paradigm brings challenges due to a mismatch between terminal reward optimization and the underlying generative dynamics. As a result, optimization tends to exploit stochastic degrees of freedom, often improving reward at the expense of image fidelity. To address this issue, we propose Diff-Instruct with Diffused Reward (DIDR), a data-free trajectory-level alignment framework derived from Integral KL minimiza...
| Authors | Junyi Wu, Weijian Luo, Haoyang Zheng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 May 2026 |
| Last updated at source | 26 May 2026 |
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