SimpleVLN: Towards Simple and Frontier Vision-Language Navigation

Key points

• As multimodal large language models (MLLMs) become increasingly capable, an important question remains: Have we truly unlocked the potential of native MLLMs for VLN before introducing task-specific architecture?1
• We present SimpleVLN, an architecture-preserving framework developed through studies of navigation formulation, data construction, and optimization.1
• At the formulation level, SimpleVLN combines single-turn decision inputs with fixed-budget global-local memory to efficiently use navigation history without unbounded context growth, while action chunks enable efficient output of multiple primitive actions per model invocation.1
• At the optimization level, a progressive IL-DAgger-RL pipeline advances from action imitation to task-level outcome optimization.1

Sources (1)

• [1]SimpleVLN: Towards Simple and Frontier Vision-Language Navigation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 09:41 AM

  Vision-and-Language Navigation (VLN) requires embodied agents to ground language in visual observations and make long-horizon decisions.

  As multimodal large language models (MLLMs) become increasingly capable, an important question remains: Have we truly unlocked the potential of native MLLMs for VLN before introducing task-specific architecture?

Extractive summary: sentences quoted from the sources.