Rethinking Simplicity in Transformer Feedforward Networks: \\ An Empirical Study of Minimal Gating

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Abstract:This paper presents a systematic investigation of minimal gating mechanisms for transformer feedforward networks. While complex gating approaches like SwiGLU and GEGLU dominate current architectures, we rigorously evaluate whether simpler alternatives can offer comparable performance. Through extensive ablation studies and careful analysis of 10 different gating variants, we demonstrate that our minimal gating approach achieves a validation loss of 5.167 on the FineWeb dataset, representing a 4.9\% degradation compared to SwiGLU (4.927). We provide detailed empirical evidence of the tradeoffs between simplicity and performance, including optimization dynamics and computational efficiency metrics. Our results suggest that while minimal gating underperforms state-of-the-art approaches, it may offer advantages in scenarios prioritizing interpretability and training stability over absolute performance.