ActionGuide: Learning Transferable Action-Grounded Reasoning for Closed-Loop Driving

We introduce **ActionGuide-Bench**, a unified evaluation framework for connecting action-grounded reasoning in Multimodal Large Language Models (MLLMs) to closed-loop driving utility. Moving beyond isolated open-loop judgments, ActionGuide-Bench evaluates leading MLLMs at two complementary levels: an action-grounded evaluation comprising Evidence Grounding, Consequence Prediction, Trajectory Verification, and Corrective Guidance, and a closed-loop evaluation measuring how these capabilities influence planner decisions during execution. ActionGuide-Bench casts the MLLM as an online guide to a frozen base planner, verifying proposed trajectories and providing corrective guidance when the available behaviors are unsuitable, thereby exploiting the utility of MLLM judgments without retraining the underlying motion policy. To strengthen these capabilities, we further curate **ActionGuide-Train**, which connects decision-critical contexts with alternative ego behaviors, execution outcomes, and corrective actions through rule-based synthesis and planner rollouts. Comprehensive evaluation reveals three findings: (1) **Effective Guidance from MLLMs:** pretrained open-source MLLMs can already improve closed-loop driving as online guides, despite limited action-grounded reasoning; (2) **Transferable Behavior Knowledge:** behavior-level driving knowledge learned from simulation transfers effectively to real-world scenes; and (3) **Capability-Utility Alignment:** stronger and more balanced action-grounded competence is closely associated with better closed-loop utility. Together, these results position action-grounded reasoning as a key interface between multimodal understanding and concrete action control, translating visual-semantic knowledge into effective closed-loop behavior.