GuidedWAM: Training-Free Energy Guidance for World Action Models in Autonomous Driving

Authors: Xiyan Su , Finn Rasmus Schäfer , Hojun Lim , Frank Diermeyer , Johannes Betz , Markus Lienkamp

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Abstract: World action models (WAMs) for autonomous driving couple the driving policy and the visual world model in a single generative network whose objectives are fixed post-training. Conditioning on a new driving objective requires retraining. We study energy guidance as a general mechanism and present GuidedWAM, a model-agnostic framework for injecting driving objectives into frozen WAMs at sampling time. Our framework constructs each objective as differentiable energies on the sampler's intermediate clean-action estimate, which needs only forward access to the frozen model, and is stabilized by sampling-time guidance scheduling, per-energy normalization, and velocity-bounded correction. On the NAVSIM v2 hard-split, guidance improves all three frozen, publicly released WAMs by 1.8 to 3.6 EPDMS, averaged over three sampler seeds, and adds less than 10% to the sampling time per step. These results position energy guidance as a practical post-training control channel for WAMs.

Subjects:

Artificial Intelligence (cs.AI) ; Robotics (cs.RO)

Cite as:

arXiv:2611.07912 [cs.AI]

(or

arXiv:2611.07912v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.07912

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arXiv-issued DOI via DataCite (pending registration)

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From: Xiyan Su [ view email ]

[v1]

Thu, 5 Nov 2026 12:27:38 UTC (963 KB)

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