Video diffusion has long been stuck on an old pair of contradictions: to make frames "look more right" with RLHF, you have to add a reward model; to compress 50 denoising steps to 4, you have to distill. The two goals pull against each other in two different representation spaces — change one and the other breaks. ECCV 2026's accepted Reward Lightning (arXiv:2607.03960) welds scoring and distilling into the same backbone. The core is a latent-space reward model (LRM) that scores videos directly in the diffusion's latent space, no longer detouring back to pixel space; same-source preference distillation (HPD) based on LRM lets discrimination and generation share weights, fundamentally eliminating gradient conflict. With just 1-4 steps, the method generates high-fidelity video, with VBench improving 2.1% on average and leading the existing methods on all three sub-metrics of text alignment, motion quality, and visual quality; LRM itself also beats the pixel-level and latent-space-level reward baselines by 11.0% and 14.7% respectively. This alignment + acceleration same-source design will almost certainly become the standard paradigm for the next generation of video diffusion distillation in Wan, LongCat-Video, Cosmos, and the like.