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arXiv · 2610.34689

RSKD: Grounding Semantic Consistency in Driving VLMs via Distillation

Chuchu Xie·Yang Zhong·Kangan Qian·Hao Ye·Kun Jiang·Diange Yang
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Abstract

Knowledge distillation can transfer large multimodal models to compact models for autonomous driving, but uniform token-level matching can dilute planning-relevant semantics and amplify teacher errors. We propose RSKD, a semantic-consistency-guided distillation framework that routes supervision according to both the task importance of an answer phrase and its semantic consistency with the teacher prediction. Task-critical semantic phrases are localized in the ground-truth answer, and a phrase-level consistency score controls the balance between Jensen--Shannon divergence (JSD) distillation and ground-truth supervision. Teacher information is emphasized only where it is semantically consistent, while semantically inconsistent phrases receive stronger ground-truth supervision. We evaluate RSKD on DrivingVQA, LingoQA, and MapLMv2. RSKD improves over the compared student-based baselines on DrivingVQA and LingoQA and remains competitive on MapLMv2.

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Chuchu Xie, Yang Zhong, Kangan Qian, Hao Ye, Kun Jiang, Diange Yang. 2026-10-26. RSKD: Grounding Semantic Consistency in Driving VLMs via Distillation. https://arxiv.org/abs/2610.34689

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