AI 中文总结
针对大规模VLN模型部署成本高的问题，提出通过可学习查询槽提取导航证据并蒸馏至紧凑学生模型，参数量减少93.65%且性能几乎持平。
AI 中文摘要
近期的大规模视觉-语言导航（VLN）模型虽然具有较高的准确率，但由于其参数量大和计算需求高，部署成本高昂。我们分两步解决高效VLN问题。首先，我们构建一个高性能的教师模型，使导航证据选择变得明确且可压缩。该教师模型通过可导航查询生成器引入一组小型可学习查询槽，从全景观测中提取全局和局部的动作充分的可导航证据，然后通过指令-查询对齐器将这些证据标记逐步与指令进行对齐，用于策略预测。其次，利用这一显式查询瓶颈作为蒸馏接口，我们通过迁移“关注哪里”和“做什么”来训练一个紧凑的学生模型。我们使用导航感知的令牌自适应目标蒸馏教师的全局和局部可导航查询，并在微调期间进一步匹配动作分布。在标准VLN基准上的实验表明，我们的学生模型在导航性能上几乎与教师模型持平，同时参数量相比教师模型减少了93.65%。
英文摘要
Recent large-scale Vision-and-Language Navigation (VLN) models deliver strong accuracy but remain costly to deploy due to their large parameter counts and computational requirements. We tackle efficient VLN in two steps. First, we build a high-performing teacher that makes navigation evidence selection explicit and compressible. The teacher introduces a small set of learnable query slots to extract global and local action-sufficient navigable evidence from panoramic observations via a Navigable Query Generator, then progressively grounds these evidence tokens to the instruction with an Instruction-Query Aligner for policy prediction. Second, using this explicit query bottleneck as a distillation interface, we train a compact student by transferring both where to attend and what to do. We distill the teacher's global and local navigable queries with a navigation-aware token-adaptive objective, then further match action distributions during fine-tuning. Experiments on standard VLN benchmarks demonstrate that our student nearly matches the teacher's navigation performance while reducing the number of parameters by 93.65% compared to the teacher.
