STEER: Route-Aware Adaptive Reasoning for Autonomous Driving
Overview
This research presents STEER, a novel strategy to mitigate over-reasoning in vision-language action (VLA) models for autonomous driving. By combining uncertainty-aware rollout routing with cross-route advantage credit assignment, the method dynamically adjusts inference complexity without requiring subjective scene-difficulty annotations. Experiments on NAVSIM v1 and v2 show that STEER achieves state-of-the-art planning quality while improving the Pareto front of inference efficiency compared to prior adaptive reasoning methods.
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“arXiv:2611.00718v1 Announce Type: new Abstract: Vision-language action (VLA) models excel for end-to-end autonomous driving, yet their tendency to over-invoke chain-of-thought (CoT) reasoning mirrors a human cognitive pitfall: overthinking. Just as deliberate reasoning can slow or mislead human judgment in routine tasks, excessive CoT in VLA incurs unnecessary compute overhead and can paradoxically degrade planning p”
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- arXiv Robotics — research abstractsSignalSTEER: Route-Aware Adaptive Reasoning for Autonomous Driving
This research presents STEER, a novel strategy to mitigate over-reasoning in vision-language action (VLA) models for autonomous driving. By combining uncertainty-aware rollout routing with cross-route advantage credit assignment, the method dynamically adjusts inference complexity without requiring subjective scene-difficulty annotations. Experiments on NAVSIM v1 and v2 show that STEER achieves state-of-the-art planning quality while improving the Pareto front of inference efficiency compared to prior adaptive reasoning methods.
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