Poster

AdaNav: Adaptive Reasoning with Uncertainty for Vision-Language Navigation

Xin Ding ⋅ Jianyu Wei ⋅ Yifan Yang ⋅ Shiqi Jiang ⋅ QIANXI ZHANG ⋅ Hao Wu ⋅ Fucheng Jia ⋅ Liang Mi ⋅ Yuxuan Yan ⋅ weijun wang ⋅ Yunxin Liu ⋅ Zhibo Chen ⋅ Ting Cao

Abstract

Vision-Language Navigation (VLN) requires agents to follow natural language instructions by grounding them in sequential visual observations over long horizons. Explicit reasoning could enhance temporal consistency and perception–action alignment, but reasoning at fixed steps often leads to suboptimal performance and unnecessary computation. To address this, we propose AdaNav, an uncertainty-based adaptive reasoning framework for VLN. At its core is the Uncertainty-Adaptive Reasoning Block (UAR), a lightweight plugin that dynamically triggers reasoning. We introduce Action Entropy as a policy prior for UAR and progressively refine it through a Heuristics-to-RL training method, enabling agents to learn difficulty-aware reasoning policies under the strict data limitations of embodied tasks. Results show that with only 6K training samples, AdaNav achieves substantial gains over closed-source models trained on million-scale data, improving success rate by 20% on R2R val-unseen, 11.7% on RxR-CE, and 11.4% in real-world scenes.