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
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- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%DietrichGebert/ponytail →
“Shared author/contributor keys: cheng”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Feinan Cheng →
“No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation”
- LinkedLinked via arxiv author · 85%Dongliang Xu →
“No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation”
- LinkedLinked via arxiv author · 85%Wenli Nong →
“No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation”
- LinkedLinked via arxiv author · 85%Zhiheng Zhang →
“No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation”