# OpTTA

Pretrained latent world models are usually frozen at test time, and distribution shift can degrade planning with them; updating only on prediction loss fits the outcomes of the agent's own actions and can stay wrong about actions that would reach the goal. OpTTA adds the planner's goal-reaching cost on candidate imagined rollouts to the prediction loss, biasing adaptation toward model parameters that are optimistic about reaching the goal. Across three JEPA world models and 15 shifted settings, adapting only the predictor raises final success over the frozen model by 13.5 percentage points on average, versus 6.9 for prediction-only AdaJEPA; with the encoder also adapted, the gains are 13.5 versus 7.5 points.

Pretrained latent world models are usually frozen at test time, and distribution shift can degrade planning with them; updating only on prediction loss fits the outcomes of the agent's own actions and can stay wrong about actions that would reach the goal. OpTTA adds the planner's goal-reaching cost on candidate imagined rollouts to the prediction loss, biasing adaptation toward model parameters that are optimistic about reaching the goal. Across three JEPA world models and 15 shifted settings, adapting only the predictor raises final success over the frozen model by 13.5 percentage points on average, versus 6.9 for prediction-only AdaJEPA; with the encoder also adapted, the gains are 13.5 versus 7.5 points.