The structural advantage of simulator-generated data is that correctness is inherent, not annotated. When a physics engine runs a scenario, the outcome is a mathematical fact — no human labels it, no crowdsourcing pipeline reviews it, no scraping operation finds it. That makes the training signal for RL essentially free at scale. The CMU Robotics Institute's Sim2Reason approach operationalizes this by placing the model inside simulated physics environments rather than feeding it static text, forcing reasoning from first principles rather than pattern-matching against memorized solutions.