EVOKE ranks the same actions under alternative goals at fixed states, pushing pretrained world knowledge into decisions: ALFWorld unseen success rises from 60.4% to 91.8%
EVOKE is a post-training method that holds the environment state and interaction history fixed while introducing alternative goals for the same candidate actions and training on contrastive rankings, forcing the policy to elicit action-consequence knowledge already present from pretraining; across ALFWorld, WebShop, and search-based QA on three backbones it achieves the best average, raising ALFWorld unseen-game success from 60.4% for πboot to 91.8% while using fewer actions.