Filtered to multimodal large language models · clear filter
This paper proposes a method to use multimodal large language models (MLLMs) to improve cinematic video generation by providing both temporal inference and factor-aware control. A practitioner might care about this paper if they want to generate more realistic and controllable videos.
This paper creates a simulated environment where a human and AI assistant interact, allowing researchers to test how well AI assistants can guide humans in the physical world. Practitioners might care because it could lead to more reliable AI assistants that can truly help people with tasks like remembering what to do.
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.
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.