⚡ Improved Training Technique for Shortcut Models got accepted at NeurIPS 2025. This paper tackle the five core issues that held shortcut models back: the hidden flaw of compounding guidance, inflexible fixed guidance, frequency bias, divergent self-consistency, and curvy flow trajectories. Our method achieves state-of-the-art FID scores, making shortcut models a viable class of generative models capable of one-step, few-step, and multi-step sampling.