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DRMoET may improve load balancing in mixture-of-experts training

A post says DRMoET seems to outperform DeepSeek's aux-loss-free approach without a throughput cost.

Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞)T(
1 Source, 1h ago, first seen 1h ago

TLDR

A post highlights Distributionally Robust Mixture-of-Experts Training (DRMoET) as a load-balancing improvement that seems to do better than DeepSeek's aux-loss-free approach without a throughput cost. It also quotes a claim that DRMoET improves mixture-of-experts performance by reducing the penalty of imperfect routing.

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1.6K

1 Source, first seen 1h ago

25 likes3 comments14 saves2 reposts

Combined views

1.6K

1 Source, first seen 1h ago

25 likes3 comments14 saves2 reposts
Image: @teortaxesTex on X

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1 Source

Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞)@teortaxesTexDistributionally Robust Mixture-of-Experts Training Interesting improvement to load balancing, that seems to do even better than DeepSeek's aux-loss-free at no throughput cost. «DRMoET improves MoE performance… [also] by reducing the penalty of imperfect routing».1h
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    1 Source

    Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞)@teortaxesTexDistributionally Robust Mixture-of-Experts Training Interesting improvement to load balancing, that seems to do even better than DeepSeek's aux-loss-free at no throughput cost. «DRMoET improves MoE performance… [also] by reducing the penalty of imperfect routing».1h
    Today's Rank

    #4

    Today's Rank

    #4