This paper introduces FOCAL-VLA, a framework that enhances vision-language-action (VLA) models through subtask-guided geometry distillation and implicit world modeling. This improves VLA models' ability to learn spatial and temporal understanding, leading to better performance in robotic manipulation tasks.