From Search to Policy: Model-Based Improvement for Offline Visual Control

LeWorldModel (LeWM) provides predictive visual representations as a foundation for goal-directed control. Temporal reachability gives these representations a geometry based on the interaction cost of reaching one state from another. We introduce Directed Temporal Representations for Control (DTRC) to learn this temporal geometry from offline trajectories. Using temporal supervision from offline trajectories, we learn a control representation and a directed temporal distance on frozen LeWM features. We show that learning temporal reachability aligns latent distance with goal-reaching cost and turns distance reduction into a direct measure of action progress. We demonstrate empirically that this temporal geometry leads to higher success rates across a diverse suite of visual goal-conditioned control tasks.