Simplifying Gated Feedforward Networks

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Abstract:We investigate simplified gated feedforward networks as an alternative to complex gating mechanisms in transformer architectures. Our approach reduces implementation complexity while attempting to preserve performance benefits of gated activations. Through comprehensive evaluation on FineWeb using an 83M parameter Qwen 3 architecture, we find that our simplified method achieves competitive performance (4.940 validation loss) compared to established baselines, outperforming IsoGMLP while showing modest degradation compared to SwiGLU (4.927). Despite increased memory usage (27\% overhead), our approach demonstrates stable training dynamics and implementation simplicity. We provide detailed analysis of computational tradeoffs and discuss practical limitations, contributing to understanding of performance-complexity relationships in gated feedforward architectures. Our results suggest that architectural minimalism can maintain competitive performance in certain settings, though careful evaluation of tradeoffs remains essential.