Jointly Training Large Autoregressive Multimodal Models
Authors: name: Emanuele Aiello, name: Lili Yu, name: Yixin Nie, name: Armen Aghajanyan, name: Barlas Oguz

In recent years, advances in the large-scale pretraining of language and
text-to-image models have revolutionized the field of machine learning. Yet,
integrating these two modalities into a single, robust model capable of
generating seamless multimodal outputs remains a significant challenge. To
address this gap, we present the Joint Autoregressive Mixture (JAM) framework,
a modular approach that systematically fuses existing text and image generation
models. We also introduce a specialized, data-efficient instruction-tuning
strategy, tailored for mixed-modal generation tasks. Our final instruct-tuned
model demonstrates unparalleled performance in generating high-quality
multimodal outputs and represents the first model explicitly designed for this
purpose.