Synthetic video data with verifiable rewards offers a scalable source of supervision for temporal motion understanding in video-language models, yet it remains unclear which synthetic tasks transfer reliably to real-video benchmarks.We investigate this question through a diagnostic framework that treats each synthetic task as a controlled intervention.Starting from a shared Qwen3-VL-2B checkpoint with frozen vision encoder, we independently train nine programmatic motion tasks with RLVR and evaluate each resulting model across six temporal-motion dimensions, such as direction, rotation, and velocity, yielding an interference matrix of cross-dimensional transfer. The matrix reveals substantial variation in transfer that surface-level label alignment fails to explain: a frame-to-frame velocity task yields a large gain on its target dimension, whereas a nominally aligned trajectory-shape task degrades the dimension it is intended to improve. We find that transfer is better explained by computational alignment, namely whether the training task induces the local, order-sensitive temporal computation that the downstream capability requires.Counterfactual tasks that share semantic targets but differ in computational demands support this account, and the patterns hold on InternVL3-2B. We further introduce a training-free answer-invariance test under frame shuffling that flags shortcut-prone tasks prior to RLVR training. Using the interference matrix to filter tasks yields a task mixture that consistently outperforms uniform mixing on TOMATO, with positive transfer trends on MVBench, TempCompass, and TemporalBench.Our framework thus reframes synthetic video generation as an empirically grounded task-selection problem.