Researchers propose CALOS, a runtime safety layer for safe deep reinforcement learning in quadrotor control. CALOS enforces attitude constraints without modifying the underlying learning algorithm, using a quadratic program to compute the minimum-norm correction to the nominal torque output. This improves performance by 55-60% and accelerates training convergence.