Before revision:  The proposed framework alternates between updating a policy and inferring a reward function. While the policy update is performed with the inferred, potentially dense reward function, the original sparse reward is used to provide a self-supervisory signal for the reward update by serving as an ordering over the observed trajectories .
After revision:  The proposed framework alternates between classification-based reward inference and policy update steps -- the original sparse reward is used to provide a self-supervisory signal for the reward update by serving as an ordering over the observed trajectories .