This way of using the Mixer allows us to work with limited amounts of data and still get better results than what was achieved with Transformers. This is because Mixers seem to be more stable during training and seem to pay attention to all the inputs, while Transformers tend to collapse and pay attention only to some modalities/parts of the sequence.
Acknowledgements: We would like to thank the Meta AI Research and Partner Engineering teams for this collaboration.