IMWM: Intuition Models Complement World Models for Latent Planning

Planning with a learned latent world model is a promising route to control from raw pixels, and when such a planner fails, the usual remedy is a better world model. We show experimentally that this is not always enough: even with a perfect world model—obtained by replacing the world model with the simulator itself, which can predict the outcome of any action sequence with zero error—a planner with a finite sampling budget still often fails to reach the goal. This can happen because planning involves two coupled operations: sampling candidate action sequences and evaluating them. A perfect world model removes prediction error from evaluation, but sampling successful action sequences can still be difficult. Motivated by this limitation, we propose IMWM (Intuition Model + World Model), which pairs the world model with an intuition model trained on demonstrations to recognize promising action sequences. Three lightweight components connect them: (i) Retrieval Initialization guides sampling by starting it from a retrieved demonstration; (ii) Hybrid Cost adds the intuition score to evaluation, alongside the world model's goal loss; (iii) Reliability Gate decides, before planning, whether to enable the intuition model. Experiments on six pixel-based goal-reaching tasks show that IMWM improves on or matches the world-model-only planner, with large gains on OGBench-Cube (93.2%, +28.5 percentage points) and Two-Room (99.3%, +10.0 points). On OGBench-Scene and DMC-Cheetah, two tasks added after all planner settings had been fixed, it gains 7.5 and 20.5 points, respectively.