This paper challenges common assumptions about why generative models succeed in robotic control, finding that their advantage comes from iterative computation with proper supervision and stochasticity rather than multi-modal distribution capture. A lightweight two-step regression-based policy matches or exceeds performance of more complex generative control policies, suggesting the distribution-fitting component is less critical than believed.