# Contrastive Action Feedback

UniSkill lets a shared policy act as both the actor that executes environment actions and the proposer that emits skillbank edits (Add, Update, No Edit), and trains the proposer with contrastive action feedback that measures how replacing the retrieved skill changes the current actor's action log-likelihood gap between previously collected successful and failed trajectories, avoiding extra rollouts per proposal, with skill-edit support regularization preserving exploration; it reaches 98.4% success on ALFWorld and 84.7% success on WebShop while sustaining stable joint training over 250 steps.

UniSkill lets a shared policy act as both the actor that executes environment actions and the proposer that emits skillbank edits (Add, Update, No Edit), and trains the proposer with contrastive action feedback that measures how replacing the retrieved skill changes the current actor's action log-likelihood gap between previously collected successful and failed trajectories, avoiding extra rollouts per proposal, with skill-edit support regularization preserving exploration; it reaches 98.4% success on ALFWorld and 84.7% success on WebShop while sustaining stable joint training over 250 steps.