arXiv 2506.015512025-10-14cs.CVcs.AIcs.CL

EvolveNav：通过自我改进的具身推理增强基于LLM的视觉语言导航

EvolveNav: Empowering LLM-Based Vision-Language Navigation via Self-Improving Embodied Reasoning

Shanghai Jiao Tong University（上海交通大学）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Peng Cheng Laboratory（鹏城实验室）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Mohamed Bin Zayed University of Artificial Intelligence（Mohamed Bin Zayed人工智能大学）；Yinwang Intelligent Technology Co.（亿维智能技术有限公司）

Bingqian Lin, Yunshuang Nie, Khun Loun Zai, Ziming Wei, Mingfei Han, Rongtao Xu, Minzhe Niu, Jianhua Han, Hanwang Zhang, Liang Lin, Bokui Chen, Cewu Lu, Xiaodan Liang

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AI总结：

EvolveNav提出两阶段自我改进训练范式，通过形式化CoT微调和自我反思后训练增强LLM在视觉语言导航中的推理能力，在多个基准上超越现有方法。

摘要：

近期研究揭示了训练开源大型语言模型（LLMs）以释放其推理能力来提升视觉语言导航（VLN）性能的潜力，同时缓解LLMs训练语料与VLN任务之间的领域差距。然而，这些方法主要采用直接的输入-输出映射范式，导致映射学习困难且导航决策不可解释。思维链（CoT）训练是提高导航决策准确性和可解释性的有前途的方法，但导航任务的复杂性使得完美的CoT标签不可用，并且通过纯CoT监督微调可能导致过拟合。为解决这些问题，我们提出了EvolveNav，一种新颖的自我改进的具身推理范式，实现适应性和泛化的导航推理，以增强基于LLM的视觉语言导航。具体而言，EvolveNav包括两阶段训练过程：（1）形式化CoT监督微调，我们使用精心策划的形式化CoT标签训练模型，首先激活模型的导航推理能力，同时提高推理速度；（2）自我反思后训练，模型使用其自身推理输出作为自我丰富的CoT标签进行迭代训练，以增强监督多样性。还设计了一个自我反思辅助任务，鼓励模型通过与错误推理模式对比来学习正确的推理模式。在任务特定和跨任务训练范式下的实验结果表明，EvolveNav在各种流行基准（包括R2R、REVERIE、CVDN和SOON）上相对于先前的基于LLM的VLN方法具有一致的优越性。代码可在https://github.com/expectorlin/EvolveNav获取。

英文摘要：

Recent studies have revealed the potential of training open-source Large Language Models (LLMs) to unleash LLMs' reasoning ability for enhancing vision-language navigation (VLN) performance, and simultaneously mitigate the domain gap between LLMs' training corpus and the VLN task. However, these approaches predominantly adopt straightforward input-output mapping paradigms, causing the mapping learning difficult and the navigational decisions unexplainable. Chain-of-Thought (CoT) training is a promising way to improve both navigational decision accuracy and interpretability, while the complexity of the navigation task makes the perfect CoT labels unavailable and may lead to overfitting through pure CoT supervised fine-tuning. To address these issues, we propose EvolveNav, a novel sElf-improving embodied reasoning paradigm that realizes adaptable and generalizable navigational reasoning for boosting LLM-based vision-language Navigation. Specifically, EvolveNav involves a two-stage training process: (1) Formalized CoT Supervised Fine-Tuning, where we train the model with curated formalized CoT labels to first activate the model's navigational reasoning capabilities, and simultaneously increase the reasoning speed; (2) Self-Reflective Post-Training, where the model is iteratively trained with its own reasoning outputs as self-enriched CoT labels to enhance the supervision diversity. A self-reflective auxiliary task is also designed to encourage the model to learn correct reasoning patterns by contrasting with wrong ones. Experimental results under both task-specific and cross-task training paradigms demonstrate the consistent superiority of EvolveNav over previous LLM-based VLN approaches on various popular benchmarks, including R2R, REVERIE, CVDN, and SOON. Code is available at https://github.com/expectorlin/EvolveNav.

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