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UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City

106 upvotes · 27 AUG 2026 · Tianjie Ju, Zheng Wu, Yueqing Sun et al.

This paper explores how large language models can turn local observations of a city into reliable actions, and whether these models can sustain goal-directed behavior in complex urban environments. Practitioners in AI/ML and urban planning might care about the limitations and potential of current models in navigating real-world cities.

multimodal large language modelsurban agencyspatial reasoningopen-ended environmentscontinual learning

Annotations as Rollouts: Efficient and Scalable Reinforcement Learning for Video MLLMs

98 upvotes · 20 AUG 2026 · Yunheng Li, Guohong Mu, Hao Li et al.

This paper introduces a method called OraRL to improve the efficiency and scalability of reinforcement learning for multimodal large language models (MLLMs) trained on video data. By leveraging annotations as a source of high-quality rollouts, OraRL can significantly reduce the number of required rollouts, leading to faster training times and better performance on video understanding tasks.

reinforcement learningmultimodal large language modelsvideo perceptionoracle rolloutschain-of-thought generation