Could privileged information help reinforcement learning find useful rollouts?

A post introducing “Pedagogical RL” argues that typical reinforcement-learning algorithms and on-policy distillation use privileged information to score rollouts, but not to find them. It asks whether that information could instead help sample rollouts that reinforcement learning might otherwise stumble upon through more computation.