  Despite the promise of Mixture of Experts (MoE) models in increasing
parameter counts of Transformer models while maintaining training and inference
costs, their application carries notable drawbacks. The key strategy of these
models is to, for each processed token, activate at most a few experts -
subsets of an extensive feed-forward layer. But this approach is not without
its challenges. The operation of matching experts and tokens is discrete, which
makes MoE models prone to issues like training instability and uneven expert
utilization. Existing techniques designed to address these concerns, such as
auxiliary losses or balance-aware matching, result either in lower model
performance or are more difficult to train. In response to these issues, we
propose Mixture of Tokens, a fully-differentiable model that retains the
benefits of MoE architectures while avoiding the aforementioned difficulties.
Rather than routing tokens to experts, this approach mixes tokens from
different examples prior to feeding them to experts, enabling the model to
learn from all token-expert combinations. Importantly, this mixing can be
disabled to avoid mixing of different sequences during inference. Crucially,
this method is fully compatible with both masked and causal Large Language
Model training and inference.
