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yl4300
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This repository contains the model presented in TAU-R1: Visual Language Model for Traffic Anomaly Understanding.
TAU-R1 is a two-layer vision-language framework for Traffic Anomaly Understanding (TAU). The first layer is a lightweight anomaly classifier that performs coarse anomaly categorisation, while the second layer is a larger anomaly reasoner that generates detailed event summaries. It is trained with a two-stage strategy consisting of decomposed-QA-enhanced supervised fine-tuning followed by TAU-GRPO, a GRPO-based post-training method with TAU-specific reward functions.