# RSKD

Knowledge distillation for driving vision-language models previously matched teacher distributions uniformly across answer tokens, diluting planning-relevant semantics and passing on confident teacher errors. RSKD first localizes task-critical phrases in the ground-truth answer, such as traffic participants, states and spatial relations, then uses a frozen sentence encoder to score how semantically consistent the teacher's prediction is with each phrase, so consistent positions take more teacher-distribution supervision and inconsistent ones take more ground-truth supervision. On DrivingVQA, LingoQA and MapLMv2, RSKD reaches 16.60, 0.672 and 81.82, beating the compared SFT, GKD and ToDi student baselines on the two QA benchmarks and nearly matching SFT's 81.85 on MapLMv2.

Knowledge distillation for driving vision-language models previously matched teacher distributions uniformly across answer tokens, diluting planning-relevant semantics and passing on confident teacher errors. RSKD first localizes task-critical phrases in the ground-truth answer, such as traffic participants, states and spatial relations, then uses a frozen sentence encoder to score how semantically consistent the teacher's prediction is with each phrase, so consistent positions take more teacher-distribution supervision and inconsistent ones take more ground-truth supervision. On DrivingVQA, LingoQA and MapLMv2, RSKD reaches 16.60, 0.672 and 81.82, beating the compared SFT, GKD and ToDi student baselines on the two QA benchmarks and nearly matching SFT's 81.85 on MapLMv2.