AnyGoal: On World Model Generalization Beyond Expert Rollouts

The goal of a world model is to capture the dynamics of an environment. This has the great appeal that it allows agents to plan and simulate a course of actions with the help of a world model. For these uses to be reliable, however, the learned dynamics must behave well beyond the specific state–action combinations observed in expert rollouts. In this work, we show that current world models broadly struggle to generalize to new goals in known environments. We explain this from the theoretical perspective of compositional generalization, which allows us to formulate desiderata and conditions of successful generalization. Based on these insights, we construct a new benchmark, AnyGoal. We carefully check that each tasks remain within the factor-wise support of the training data, so that they can be reasonably expected to be solvable by a good world model. By disentangling encoder–predictor failures, generative versus latent planning models, interaction-free versus interaction-rich regimes, and other axes of variation, our results reveal both where current world models can generalize and where major challenges remain for goal-shifted dynamics prediction and planning. This framework and benchmark will provide direction and a progress measure for the goal of building world models that generalize to new planning tasks.