GeoAgent：通过强化地理特征学习实现全球地理定位

摘要

本文提出GeoAgent模型，该模型能够进行类人精细推理并得出细粒度地址结论。尽管基于强化学习的现有方法在性能与可解释性方面取得突破，但由于其依赖AI生成的思维链数据及与地理特性相冲突的训练策略，仍存在隐忧。为解决这些问题，我们首先推出GeoSeek——一个由地理专家与专业玩家共同标注思维链数据的新型地理定位数据集。我们深入挖掘地理任务的内在特性，提出通过一致性智能体评估的地理相似性奖励与一致性奖励机制，以辅助模型训练。这促使模型从地理视角向正确答案收敛，同时保障推理过程的完整性与一致性。实验结果表明，GeoAgent在多个粒度上超越现有方法及一系列通用视觉语言大模型，且生成的推理过程与人类思维高度契合。

English

This paper presents GeoAgent, a model capable of reasoning closely with humans and deriving fine-grained address conclusions. Previous RL-based methods have achieved breakthroughs in performance and interpretability but still remain concerns because of their reliance on AI-generated chain-of-thought (CoT) data and training strategies, which conflict with geographic characteristics. To address these issues, we first introduce GeoSeek, a new geolocation dataset comprising CoT data annotated by geographic experts and professional players. We further thoroughly explore the inherent characteristics of geographic tasks and propose a geo-similarity reward and a consistency reward assessed by a consistency agent to assist training. This encourages the model to converge towards correct answers from a geographic perspective while ensuring the integrity and consistency of its reasoning process. Experimental results show that GeoAgent outperforms existing methods and a series of general VLLMs across multiple grains, while generating reasoning that closely aligns with humans.

GeoAgent：通过强化地理特征学习实现全球地理定位

GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics

摘要

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