BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving
Paper • 2606.08684 • Published • 2
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Official Training Data of BLUE
TLDR: Driving VLAs often generate language reasoning that is useless or even harmful to driving. BLUE addresses this by generating language only when it clearly helps, thereby improving driving performance while reducing inference latency. BLUE uses a 0.11M-parameter gate to decide at each frame whether to predict driving actions with or without intermediate language generation.
Links
- Paper: https://arxiv.org/abs/2606.08684
- GitHub Repository: https://github.com/George-Ling3/BLUE
- Project Page: https://blue-website.github.io
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