AI RESEARCH
CreFlow: Corrective Reflow for Sparse-Reward Embodied Video Diffusion RL
arXiv CS.CV
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ArXi:2605.14274v1 Announce Type: new Video generation models trained on heterogeneous data with likelihood-surrogate objectives can produce visually plausible rollouts that violate physical constraints in embodied manipulation. Although reinforcement-learning post-