1. Introduction
Vision-Language Navigation (VLN) tasks, where an agent navigates an environment based on natural language instructions, present significant challenges and require complex deep learning models. However, the high computational demands and extensive training times of current VLN models often limit research accessibility and practical deployment. This work proposes Efficient-VLN to overcome these limitations by offering a more training-efficient solution. The models used in this article include Efficient-VLN (our proposed model), Speaker-Follower, and EnvDrop as baseline comparisons.
2. Related Work
Existing literature in Vision-Language Navigation has seen a proliferation of sophisticated models, many of which achieve high success rates by leveraging powerful encoders and complex attention mechanisms. Architectures like Speaker-Follower and EnvDrop have demonstrated strong performance on benchmark datasets but typically incur substantial training costs, often requiring days of GPU computation. Efforts to improve efficiency in related fields, such as image recognition and natural language processing, highlight the potential for similar gains in VLN by focusing on model compression, knowledge distillation, or more optimized training routines.
3. Methodology
Efficient-VLN is built upon a streamlined agent architecture that minimizes redundant computations while retaining critical information pathways for vision and language processing. The model employs a novel attention mechanism designed for efficient cross-modal understanding and a refined reinforcement learning training pipeline that accelerates convergence. Key methodological steps involve a multi-stage training process combining pre-training on synthetic data with fine-tuning on real-world datasets, and the integration of a lightweight policy network optimized for fast inference. Our workflow also includes a custom data augmentation strategy to further enhance generalization.
4. Experimental Results
Our experimental evaluation demonstrates that Efficient-VLN significantly reduces training time while maintaining competitive performance across standard VLN metrics. On the R2R dataset, Efficient-VLN achieved a higher Success Rate and lower Navigation Error compared to baseline models, with a remarkable reduction in GPU-hours. These findings underscore the effectiveness of our proposed efficiency-driven design principles without sacrificing navigation accuracy. The table below summarizes the key performance indicators and training efficiencies.
5. Discussion
The results confirm that training efficiency can be substantially improved in Vision-Language Navigation without compromising task performance, challenging the notion that more complex models are always better. The success of Efficient-VLN suggests that future VLN research can benefit from focusing on optimized architectures and training paradigms, enabling faster iteration and wider accessibility. This efficiency opens new avenues for deploying VLN agents in resource-constrained environments and for conducting extensive hyperparameter searches or ablation studies that were previously prohibitively expensive.