Feb. 9, 2024, 5:43 a.m. | Zane Durante Bidipta Sarkar Ran Gong Rohan Taori Yusuke Noda Paul Tang Ehsan Adeli Shrinidhi K

cs.LG updates on arXiv.org arxiv.org

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 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. Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction, enabling a versatile and adaptable AI framework. We demonstrate …

agent agents ai agents applications artificial artificial intelligence cs.ai cs.lg cs.ro datasets development domains dynamic foundation foundation model intelligence interactive novel paradigm systems tasks training training ai

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