Researchers propose CURB, a reward-shaping framework to mitigate retaliatory algorithmic collusion in reinforcement learning agents. CURB detects and penalizes collusion by measuring the total variation distance between an agent's action distributions across cooperation and defection histories. This approach is guaranteed to convert any collusive fixed point into a trivial one, preventing sustained collusive equilibria.