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moe_aux_loss_normalization
performance-moe-aux-loss-normali-auxiliary-balancing-loss-in-moe-models-e-g-olmoe-g-8f8afaaa
IFAuxiliary balancing loss in MoE models (e.g., OLMoE, GPT-Oss) uses f_i = (N/T) * sum(1{topk}) which sums to K instead of 1 when K > 1, causing loss to be too high by factor K.
THENNormalize f_i by dividing by K (top_k) in the expert mask aggregation. Ensure that the sum over experts of f_i equals 1, matching the scale of P_i. This fixes overestimation of the auxiliary load balancing loss.
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