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arXiv · 2610.26545

ReInsVLN: Revisiting Historical Evidence for Training-Free Vision-and-Language Navigation

Siwei Cheng·Hao Wu·Zitong Qi·Xinming Zhang·Haoyu Xi·Peiran Yin·Haozhe Hu·Xudong Wang·Xiaoyu Shen·Wei Zhang
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Abstract

Vision-and-language navigation (VLN) relies on retaining and using relevant observations. Yet growing histories introduce redundancy, and retained information may remain underused. We introduce ReInsBench, a diagnostic benchmark that examines history utilization through controlled continuation trials. Its results show that history-review reinstruction can recover navigation failures using the existing task context. Motivated by this finding, we propose ReInsVLN, a training-free framework supporting memory construction and utilization in frozen navigators. Coverage- and Instruction-Aware Compression constructs compact visual context through temporal redundancy reduction, coverage-aware protection, and instruction-guided token selection. Forgetting-Inspired History Reminders guide navigators to revisit retained observations according to a predefined schedule, without additional model calls. On R2R Val-Unseen, ReInsVLN improves success rates by 8.48 and 3.37 percentage points over the native StreamVLN and InternVLA-N1, reaching 64.27% and 64.44%, respectively. Visual compression largely preserves navigation performance while reducing prefill latency and KV cache memory by up to 21.77% and 32.25%, respectively, on matched observation workloads. Real-world trials on humanoid and quadruped robots further support the applicability of ReInsVLN to sequential instruction following and ordinal navigation.

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Siwei Cheng, Hao Wu, Zitong Qi, Xinming Zhang, Haoyu Xi, Peiran Yin, Haozhe Hu, Xudong Wang, Xiaoyu Shen, Wei Zhang. 2026-10-19. ReInsVLN: Revisiting Historical Evidence for Training-Free Vision-and-Language Navigation. https://arxiv.org/abs/2610.26545

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