ExAbs: Learning Execution-Aware Abstractions for Hierarchical World Models

Hierarchical world models extend planning horizons by letting a high-level model propose waypoints for a short-horizon controller to reach. Their temporal abstractions, however, are typically learned from offline trajectories without regard to the controller that must realize them: a transition that is easy to predict may be infeasible for the controller within its budget, and similarity between full latent states can overlook the specific change a waypoint requests. We propose execution-aware abstraction learning (ExAbs), in which the controller shapes both what the hierarchy learns to request and how each request is pursued. For the former, an Achievability Model (AM) learns from closed-loop execution the probability that the controller completes a waypoint within a given budget; AM selects the segment boundaries that train the high-level model and scores candidate waypoints during planning. For the latter, Planner-Executor Transition Abstraction Learning (PETAL) distills the decisions of a frozen planner into compact, source-relative effect representations, so that local action selection compares requested and predicted changes rather than endpoint proximity alone. Collecting AM labels with the PETAL-enabled controller ties the learned temporal units to the executor that carries them out. Across seven simulated tasks on four world-model backbones and three physical-robot tasks, ExAbs improves success over the hierarchical baseline on every task without lengthening the local planning horizon, e.g., from 77.5% to 90.0% on hard Maze queries and from 0% to 21.7% on a sequential Button–Window task, while also shortening successful executions.