Microsoft Research has proposed an Interactive Agent Foundation Model for training AI agents across diverse domains, datasets, and tasks. The model uses a multi-task agent training paradigm and encompasses pre-training strategies such as visual masked auto-encoders, language modeling, and next-action prediction. It aims to generate contextually relevant outputs in Robotics, Gaming, and Healthcare, transitioning from static, task-specific models to dynamic, agent-based systems capable of performing well in various applications.
Microsoft just dropped: An Interactive Agent Foundation Model "The development of artificial intelligence systems is transitioning from creating static, task-specific models to dynamic, agent-based systems capable of performing well in a wide range of applications. We propose an… https://t.co/zMg5zrAH55
An Interactive Agent Foundation Model from @MSFTResearch "We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents across a wide range of domains, datasets, and tasks. " https://t.co/01Z82N2e0p https://t.co/aaJxpmx2L0
An Interactive Agent Foundation Model paper page: https://t.co/ATVRGLDEfV The development of artificial intelligence systems is transitioning from creating static, task-specific models to dynamic, agent-based systems capable of performing well in a wide range of applications.… https://t.co/5RzOkM9PXt
An Interactive LLM Agent -Trains AI agents across diverse pre-training strategies (visual masked auto-encoders, language modeling, next-action prediction) -Multimodal & multi-task -Generates contextually relevant outputs in Robotics, Gaming, & Healthcare https://t.co/LVPZzvHMiH https://t.co/ss006fGeat
An Interactive Agent Foundation Model Proposes an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents across a wide range of domains, datasets, and tasks https://t.co/WxDN3KZ1rf https://t.co/pJyLqqLmVd