Inverting the Bellman Equation: How Simple Goals Build World Models in AI

A deep-dive into the 2026 paper showing that model-free agents trained on a diverse set of goals implicitly encode a detailed map of their environment in their Q-values. Through P-learning, researchers reverse-engineer this hidden world model from the agent’s value function, revealing emergent concepts like velocity and basic physics intuition in continuous-control tasks such as Reacher and MountainCar, with broad implications for interpretability and adaptable AI.

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