The article discusses Mixture-of-Depths (MoD) transformers, highlighting their ability to outperform isoFLOP-optimal models while utilizing fewer FLOPs per forward pass. This results in faster and more effective training, addressing past limitations where surplus compute was needed for overtraining smaller models. The research emphasizes the significance of learned routing decisions in optimizing FLOP usage, thereby allowing models to save compute resources. The findings indicate that with appropriate use of routing and resource allocation, enhanced model size and training duration can yield substantial performance gains.
Mixture-of-Depths transformers empirically demonstrate that one can improve on isoFLOP-optimal baseline performance with models that use fewer FLOPs per forward pass, achieving both faster speeds and better performance.
To train models that are both faster and as- or better-performing than isoFLOP-optimal models, one would have to use surplus compute to overtrain smaller models, an approach still applicable with MoD transformers.
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