GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics
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.
6992825050fb2c0be47838ce
Modi Jin
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2026-02-16T15:36:23.839Z
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Modi Jin
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user
ghost233lism
6992825050fb2c0be47838cf
Yiming Zhang
6992825050fb2c0be47838d0
Boyuan Sun
6992825050fb2c0be47838d1
Dingwen Zhang
6992825050fb2c0be47838d2
MingMing Cheng
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Qibin Hou
GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics
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 achieves superior geolocation reasoning performance through a specialized dataset and reward mechanisms that ensure geographic accuracy and reasoning consistency.
chain-of-thought geolocation dataset geo-similarity reward consistency reward consistency agent geographic characteristics reasoning process