Teaching Vision-Language-Action Models What to See and Where to Look

Yuguang Yang; Canyu Chen; Zhewen Tan; Yizhi Wang; Zichao Feng; Chunyang Liu; Kehua Sheng; Juan Zhang; Linlin Yang; Baochang Zhang; Yan Wang; Bo Zhang; Xianbin Cao

arXiv · 2607.01658

Teaching Vision-Language-Action Models What to See and Where to Look

Yuguang Yang·Canyu Chen·Zhewen Tan·Yizhi Wang·Zichao Feng·Chunyang Liu·Kehua Sheng·Juan Zhang·Linlin Yang·Baochang Zhang·Yan Wang·Bo Zhang·Xianbin Cao

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Abstract

Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving. However, existing VLAs' training relies heavily on text-centric visual question answering and chain-of-thought reasoning data, which emphasizes linguistic reasoning rather than action-grounded planning. As a result, the learned representations capture semantic knowledge but lack spatial dependencies crucial for reliable trajectory prediction. We propose DriveTeach-VLA, a framework that explicitly teaches VLAs what to see and where to look. Driving-aware Vision Distillation (DVD) injects driving-specific perceptual priors into the vision encoder, while 2D Trajectory-Guided Prompts (2D-TGP) provide spatial conditioning aligned with feasible driving trajectories. Together, they form a vision-guided learning pipeline: what to see (DVD pretraining) - where to look (TGP-guided SFT) - how to act (TGP-guided GRPO). DriveTeach-VLA achieves the state-of-the-art performance on NAVSIM and nuScenes. Our code is available at: https://github.com/ShivaTeam/DriveTeach-VLA.

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Yuguang Yang, Canyu Chen, Zhewen Tan, Yizhi Wang, Zichao Feng, Chunyang Liu, Kehua Sheng, Juan Zhang, Linlin Yang, Baochang Zhang, Yan Wang, Bo Zhang, Xianbin Cao. 2026-07-02. Teaching Vision-Language-Action Models What to See and Where to Look. https://arxiv.org/abs/2607.01658

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