SDFT

The model uses itself as a teacher for on-policy learning. It generates its own outputs (its own trajectories) → learns from them again → learns including its own mistake states (on-policy) → reduces forgetting + learns new tasks better. By distilling on-policy using its own outputs, continual learning becomes possible without RL.