PolyGLU: State-Conditional Activation Routing in Transformer Feed-Forward Networks
Biological neural systems employ diverse neurotransmitters -- glutamate, GABA, dopamine, acetylcholine -- to implement distinct signal-processing modalities within shared neural circuits. In contrast, modern transformers apply a single fixed activation function across all feed-forward neurons. We introduce PolyGLU (Polychromatic Gated Linear Unit), a drop-in replacement for SwiGLU that enables each FFN neuron to dynamically route among K=4 activation functions via a differentiable mechanism combining learned static preferences with input-conditioned gating, trained end-to-end with Gumbel-Softmax. We train PolychromaticLM, a 597M-parameter transformer, on ~10B tokens using a single NVIDIA ...
| Authors | Daniel Nobrega Medeiros |
|---|---|
| Primary category | cs.LG |
| Published | 7 March 2026 |
| Last updated at source | 7 March 2026 |
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