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arXiv Machine Learning By Mingze Wang, Jinbo Wang, Yikuan Xia, Kai Shen, Shu Zhong

More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations

Read the original on arXiv Machine Learning →

The paper introduces Mixture of Activations (MoA), a token‑adaptive feedforward network design that mixes multiple activation functions using lightweight gates while sharing linear projections. It also presents learnable activations (LA) as an input‑independent variant. The authors theoretically prove that MoA strictly surpasses both fixed‑activation FFNs and LA in expressive power, and empirically demonstrate that MoA achieves lower loss and better scaling on dense and MoE language models from 0.12 B to 2 B parameters with minimal overhead.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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