Geo-R1 uses indirect proxy rewards from cross-view alignment with geolocation metadata to drive reinforcement learning, enabling zero-shot geospatial reasoning that transfers across 25+ tasks and sometimes exceeds supervised specialists.
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Unlocking Zero-Shot Geospatial Reasoning via Indirect Rewards
Geo-R1 uses indirect proxy rewards from cross-view alignment with geolocation metadata to drive reinforcement learning, enabling zero-shot geospatial reasoning that transfers across 25+ tasks and sometimes exceeds supervised specialists.