The PEER architecture uses product keys to retrieve a small set of experts for each input and combine their outputs. The design offers a sparse-routing approach to expanding model capacity.
PEER is a Mixture of Experts architecture that uses product keys to retrieve experts. Each expert is a one-neuron MLP.
For each input, the system retrieves a small set of experts. A router calculates scores that determine how to combine the retrieved outputs. The report says the architecture retrieves more than a million experts.
The described result is an approach to expanding model capacity through sparse retrieval; on its own, it does not demonstrate gains for every task or environment. When studying or applying the idea, compare it with alternatives using metrics and tests suited to your use case.
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