No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation
Test-time scaling offers a promising method to improve the inference performance of Vision-Language Models (VLMs) without additional training. Existing approaches to vision-language navigation (VLN) for Unmanned Aerial Vehicle (UAV) typically relies on a single inference pass, which can falter in complex environments by producing suboptimal or unsafe trajectories. In this paper, we explore a simple and effective approach to apply test-time scaling to VLN for UAV. We enhance navigation reasoning through an iterative refinement process that requires no extra model training, guiding the model to re-evaluate its initial navigation plan for better accuracy and safety. Our method first prompts ...
| Authors | Feinan Cheng, Dongliang Xu, Wenli Nong et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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