FewStepRL: Fine-tuning Few-step Diffusion Models with Reinforcement Learning
FewStepRL adapts five reinforcement-learning methods to few-step diffusion models in one modular codebase.
Few-step models predict average motion over an interval, which does not directly fit objectives designed for instantaneous motion. The framework puts those objectives into a common form and adapts their training targets. In the authors’ experiments, reward-gradient methods work best when those gradients are affordable; otherwise, DiffusionNFT-style methods generally reach high rewards in relatively few iterations. Extra sampling steps provide no systematic aggregate benefit, though some outputs visibly improve.
HF Daily Papers' note
Few-step models predict average motion over an interval, which does not directly fit objectives designed for instantaneous motion. The framework puts those objectives into a common form and adapts their training targets. In the authors’ experiments, reward-gradient methods work best when those gradients are affordable; otherwise, DiffusionNFT-style methods generally reach high rewards in relatively few iterations. Extra sampling steps provide no systematic aggregate benefit, though some outputs visibly improve.
HF Daily Papers' note
score 5