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AnyGoal: On World Model Generalization Beyond Expert Rollouts

Heejeong Nam, Divyat Mahajan, Quentin Le Lidec, Randall Balestriero, David Alexander Klindt · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26844 · cited by 0 works (OpenAlex) · open-access PDF

German

Das Ziel eines Weltmodells ist es, die Dynamik einer Umgebung zu erfassen. Dies hat den großen Anreiz, dass es Agenten erlaubt, einen Aktionsverlauf mit Hilfe eines Weltmodells zu planen und zu simulieren. Damit diese Verwendungen zuverlässig sind, muss sich die gelernte Dynamik jedoch weit über die spezifischen Zustands-Aktions-Kombinationen hinaus verhalten, die in Experten-Rollouts beobachtet werden. Dennoch bleibt die Beziehung zwischen Datenabdeckung, Dynamik-Verallgemeinerung und nachgelagerter Planungsleistung unerforscht. Wir untersuchen diese Beziehung durch die Linse der Verallgemeinerung der Zusammensetzung: Kann ein Weltmodell die bekannten Zustands- und Aktionsfaktoren in gültige Dynamik kombinieren? Um dies zu beantworten,

Machine translation (Meta M2M100); the English original follows.

Abstract

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. Despite this, the relationship between data coverage, dynamics generalization, and downstream planning performance remains underexplored. We study this relationship through the lens of compositional generalization: can a world model recombine familiar state and action factors into valid dynamics? To answer this, we introduce AnyGoal, a benchmark of goal-shifted task variants built from widely used world-model environments, together with an open-source library. Each task is designed to be within reach of a world model that has learned the underlying dynamics: individual state factors and actions remain within their training support, but their combinations are underrepresented in expert trajectories. Across multiple environments and world model families, we find that current models consistently exhibit failures under these recombinations. By examining dynamics and representation failures across model families, interaction regimes, and evaluation metrics, we study where current world models generalize and where challenges remain for goal-shifted prediction and planning. Together, this framework, benchmark, and analysis provide a basis for evaluating progress toward world models that generalize to new planning tasks.

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Topics: Computer science, Generalization, Task (project management), Action (physics), Artificial intelligence, Representation (politics)

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