We introduce LightningRL, a reinforcement learning post-training framework for block-wise diffusion Large Language Models (dLLMs) that breaks the accuracy–parallelism trade-off. Applied to SDAR-8B, LightningRL achieves 7.32 average TPF and 497.9 AUP — simultaneously improving both generation quality and inference speed.