Home Community This AI Paper 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

This AI Paper 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

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This AI Paper 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

AI development is shifting from static, task-centric models to dynamic, adaptable agent-based systems suitable for various applications. AI systems aim to collect sensory data and effectively engage with environments, a longstanding research goal. Developing generalist AI offers benefits, including training a single neural model across multiple tasks and data types. This approach is extremely scalable through data, computational resources, and model parameters.

Recent works highlight some great benefits of developing generalist AI systems by training a single neural model across various tasks and data types, offering scalability through data, compute, and model parameters. Nonetheless, challenges persist, as large foundation models often produce hallucinations and infer misinformation as a consequence of insufficient grounding in training environments. Current multimodal system approaches, counting on frozen pre-trained models for every modality, may perpetuate errors without cross-modal pre-training.

Researchers from  Stanford University, Microsoft Research, Redmond, and the University of California, Los Angeles, have proposed the Interactive Agent Foundation Model, which introduces a unified pre-training framework for processing text, visual data, and actions, treating each as separate tokens. It utilizes pre-trained language and visual-language models to predict masked tokens across all modalities. It enables interaction with humans and environments, incorporating visual-language understanding. With 277M parameters jointly pre-trained across diverse domains, it engages effectively in multi-modal settings across various virtual environments.

The Interactive Agent Foundation Model initializes its architecture with pre-trained CLIP ViT-B16 for visual encoding and OPT-125M for motion and language modeling. It incorporates cross-modal information sharing through a linear layer transformation. Because of memory constraints, previous actions and visual frames are included as input, with a sliding window approach. Sinusoidal positional embeddings are utilized for predicting masked visible tokens. Unlike prior models counting on frozen submodules, your complete model is jointly trained during pre-training.

Evaluation across robotics, gaming, and healthcare tasks demonstrates promising results. Despite being outperformed in certain tasks by other models as a consequence of less data for pre-training, the tactic showcases competitive performance, especially in robotics, where it significantly surpasses a comparative model. Fne-tuning the pre-trained model proves notably effective in gaming tasks in comparison with training from scratch. In healthcare applications, the tactic outperforms several baselines leveraging CLIP and OPT for initialization, demonstrating the efficacy of its diverse pre-training approach.

In conclusion, Researchers proposed the Interactive Agent Foundation Model, which is adept at processing text, motion, and visual inputs and demonstrates effectiveness across diverse domains. Pre-training on a combination of robotics and gaming data enables the model to proficiently model actions, even exhibiting positive transfer to healthcare tasks during fine-tuning. Its broad applicability across decision-making contexts suggests potential for generalist agents in multimodal systems, unlocking recent opportunities for AI advancement.


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Asjad is an intern consultant at Marktechpost. He’s persuing B.Tech in mechanical engineering on the Indian Institute of Technology, Kharagpur. Asjad is a Machine learning and deep learning enthusiast who’s all the time researching the applications of machine learning in healthcare.


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