# Progress-Think: Semantic Progress Reasoning for Vision-Language Navigation

arXiv: https://arxiv.org/abs/2511.17097 · HTML: https://mlsanity.com/paper/2511.17097/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2025.

## Built on by (most cited first, 12 of 12)

- [MapDream: Task-Driven Map Learning for Vision-Language Navigation](https://mlsanity.com/paper/2602.00222/) (2026)
- [SpaAct: Spatially-Activated Transition Learning with Curriculum Adaptation for Vision-Language Navigation](https://mlsanity.com/paper/2604.27620/) (2026)
- [OmniEcho: Audio-Visual Spatial Understanding for Omni-Modal Embodied Agents](https://mlsanity.com/paper/2609.23407/) (2026)
- [HoloAgent-0: A Unified Embodied Agent Framework with 3D Spatial Memory](https://mlsanity.com/paper/2606.23565/) (2026)
- [Image2Sim: Scaling Embodied Navigation via Generative Neural Simulator](https://mlsanity.com/paper/2607.05765/) (2026)
- [AdaHVLA: Adaptive Harnesses for Long-Horizon Vision-Language-Action Execution](https://mlsanity.com/paper/2609.29204/) (2026)
- [From Region Arrival to Instance-Level Grounding in Vision-and-Language Navigation](https://mlsanity.com/paper/2607.03792/) (2026)
- [Seek Before You Move: Evidence Seeking for Progress Grounding in Vision-Language Navigation](https://mlsanity.com/paper/2609.37353/) (2026)
- [AVERT-VLN: Abstention-aware Visual Error Recovery and Training for Vision-and-Language Navigation](https://mlsanity.com/paper/2609.39579/) (2026)
- [Robust Vision-Language Navigation with Awareness of Instruction-Execution Progress](https://mlsanity.com/paper/2610.13104/) (2026)
- [GuidedNav: Shaping Vision-Language Navigation Representations through Pre-action Attention and Spatial Guidance](https://mlsanity.com/paper/2610.13122/) (2026)
- [FireNav: Decision-Oriented Reconstruction Evidence Routing for Vision-Language Navigation in Indoor Fire Environments](https://mlsanity.com/paper/2610.15595/) (2026)

## Abstract (arXiv)

Vision-Language Navigation requires agents to act coherently over long horizons by understanding not only local visual context but also how far they have advanced within a multi-step instruction. However, recent Vision-Language-Action models focus on direct action prediction and earlier progress methods predict numeric achievements; both overlook the monotonic co-progression property of the observation and instruction sequences. Building on this insight, Progress-Think introduces semantic progress reasoning, predicting instruction-style progress from visual observations to enable more accurate navigation. To achieve this without expensive annotations, we propose a three-stage framework. In the initial stage, Self-Aligned Progress Pretraining bootstraps a reasoning module via a novel differentiable alignment between visual history and instruction prefixes. Then, Progress-Guided Policy Pretraining injects learned progress states into the navigation context, guiding the policy toward consistent actions. Finally, Progress-Policy Co-Finetuning jointly optimizes both modules with tailored progress-aware reinforcement objectives. Experiments on R2R-CE and RxR-CE show state-of-the-art success and efficiency, demonstrating that semantic progress yields a more consistent representation of navigation advancement.