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.
Geochain: Multimodal chain-of-thought for geographic reasoning.arXiv preprint arXiv:2506.00785, 2025
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Thinking LLMs achieve ~10 percentage points higher accuracy than non-thinking ones on RewardBench with under 2x compute overhead, outperforming augmentation strategies that cost over 8x more while also showing better bias robustness.
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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.
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Explicit Reasoning Makes Better Judges: A Systematic Study on Accuracy, Efficiency, and Robustness
Thinking LLMs achieve ~10 percentage points higher accuracy than non-thinking ones on RewardBench with under 2x compute overhead, outperforming augmentation strategies that cost over 8x more while also showing better bias robustness.