PAST: Prompt-Adaptive Sampling Termination for Efficient Diffusion Model
While diffusion models have made significant progress in text-to-image tasks, they still exhibit limitations when directly optimizing downstream objectives. Although Reinforcement Learning (RL) enables targeted optimization, existing methods are generally constrained by low-efficiency fine-tuning and sparse rewards. To address these challenges, we propose PAST, which provides differentiated rewards while adaptively regulating training episode length by jointly perceiving denoising progress and prompt difficulty. Specifically, we design an intrinsic reward paradigm to compensate for sparse extrinsic rewards and guide the model to explore paths that diverge more efficiently from noise patte...
| Authors | Renye Yan, Jikang Cheng, You Wu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
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