标题： Geo-R1：利用跨视图强化学习解锁VLM地理空间推理 显示英文标题

标题： Geo-R1: Unlocking VLM Geospatial Reasoning with Cross-View Reinforcement Learning

摘要： 我们引入了Geo-R1，这是一种以推理为中心的后训练框架，通过结合思维支架和提升来解锁视觉-语言模型中的地理空间推理能力。 在支架阶段，Geo-R1通过在合成思维链示例上的监督微调，灌输一种“地理空间思维范式”，使模型能够在不依赖昂贵的人类推理标注的情况下，将视觉线索与地理先验联系起来。 在提升阶段，它使用基于GRPO的强化学习，在弱监督的跨视图配对代理上进行训练。 这种设计提供了一个可验证且可扩展的奖励信号：教导模型捕捉和协调跨模态的特征，并利用推理进行准确预测。 Geo-R1将地理空间建模从领域预训练/监督微调扩展到以推理为主的后训练，且在各种地理空间推理基准测试中取得了最先进的性能。 我们的模型可在 https://huggingface.co/miniHui/Geo-R1 获取。 显示英文摘要

摘要： We introduce Geo-R1, a reasoning-centric post-training framework that unlocks geospatial reasoning in vision-language models by combining thinking scaffolding and elevating. In the scaffolding stage, Geo-R1 instills a ``geospatial thinking paradigm" via supervised fine-tuning on synthetic chain-of-thought exemplars, enabling models to connect visual cues with geographic priors without costly human reasoning annotations. In the elevating stage, it uses GRPO-based reinforcement learning on a weakly-supervised cross-view pairing proxy. This design supplies a verifiable and scalable reward signal: teaching models to capture and reconcile features across modalities, and harnessing reasoning for accurate prediction. Geo-R1 extends geospatial modeling from domain pretraining / supervised finetuning to reasoning-first post-training, and achieves state-of-the-art performance across various geospatial reasoning benchmarks. Our model is available at https://huggingface.co/miniHui/Geo-R1.