Equation 27 · Shrink It, Train It Small, or Search for It: The Main Strategies for Small Models, Compared
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Shazeer and colleagues stated the underlying argument for conditional computation directly: “a trainable gating network determines a sparse combination of experts to use for each example,” a mechanism they showed could scale model capacity by “over 1000x” while keeping the compute spent on any one example roughly constant [ 11 ] . The now-standard form of a sparse mixture-of-experts layer routes each token to a small top- k subset of E available experts:
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