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Towards a Data-Centric Understanding of Spatial Reasoning in Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) have been widely applied in embodied agents and robotics for vision tasks. However, MLLMs suffer from limitations in spatial reasoning and perception. Post-training techniques have shown promise for improving these capabilities; yet, there are limited data-dr
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