Learning Progress-based Automatic Curriculum Reinforcement Learning (LP-ACRL) is an automatic curriculum reinforcement learning framework for scaling quadruped locomotion to diverse rough terrain. Instead of relying on a manually designed difficulty schedule, it estimates learning progress online and shifts training toward tasks where the policy can still improve. The learned controller transfers to an ANYmal D robot, achieving fast, stable locomotion up to 2.5 m/s linear and 3.0 rad/s angular velocity across stairs, slopes, gravel, and low friction surfaces.