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About this paper
Haitao Lin, Minsi Ren, Yang Li, Xingtong Ge, Zheyuan Hu, Peiyan Hu, Tailin Wu
FewStepRL adapts reinforcement-learning objectives for few-step diffusion models, whose interval-average predictions differ from the instantaneous directions those objectives assume. It maps objectives to sample-space predictions and builds reference anchors and reward-driven updates for several methods. In held-out image-composition tests, three adaptations scored above direct-substitution baselines, but reported gaps were below GenEval2's approximate 0.03 resolution in single runs.
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