Mind the Gap: Promises and Pitfalls of Hierarchical Planning in LeWorldModel

Effective long-horizon planning using latent world models remains a formidable challenge, and temporal abstraction is a promising direction for addressing it. We investigate whether this abstraction can improve LeWorldModel on long-horizon, goal-conditioned control tasks. To this end, we introduce Hi-LeWM, an extension that freezes the pretrained low-level LeWM and adds a high-level predictor that plans over latent subgoals. We evaluate Hi-LeWM on PushT and OGBCube across increasing goal offsets; our results suggest that hierarchy does not automatically improve performance. At short horizons, the best configuration benefits from a one-step high-level plan, while longer horizons reveal a mismatch between the learned macro-action space and the inference-time distribution, which we trace to the quality of CEM-selected subgoals as the main bottleneck. This is because unconstrained search can select latent macro-actions that appear favorable under the learned model but yield poor control targets. We find that constraining the search around macro-actions encoded from training trajectories, combined with appropriate subgoal execution timing, recovers effective hierarchical regimes, improving over flat LeWM by +11.3% at medium-range horizons and +14.7% at the longest PushT horizon. Overall, temporal abstraction can benefit long-horizon planning in LeWM, but only when paired with a constrained high-level search space.