{"ID":2838523,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.17097","arxiv_id":"2511.17097","title":"Progress-Think: Semantic Progress Reasoning for Vision-Language Navigation","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2511.17097","url_pdf":"https://arxiv.org/pdf/2511.17097v2","authors":"[\"Shuo Wang\",\"Yucheng Wang\",\"Guoxin Lian\",\"Yongcai Wang\",\"Maiyue Chen\",\"Kaihui Wang\",\"Bo Zhang\",\"Zhizhong Su\",\"Yutian Zhou\",\"Wanting Li\",\"Deying Li\",\"Zhaoxin Fan\"]","published":"2025-11-21T09:52:07Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}