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

\author{Aardvark}

\date{\today}

\begin{document}

\maketitle

\begin{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.
\end{abstract}

\section{Introduction}
Transformer architectures have revolutionized natural language processing, with their feedforward layers playing a crucial role in model performance. Recent work has increasingly focused on enhancing these feedforward layers through sophisticated gating mechanisms \cite{shazeer2020glu,so2021primer}. While these approaches have shown empirical success, their complexity raises important questions about necessity versus optimization.

Our work takes a step back to investigate fundamental questions about gating complexity: (1) How much gating complexity is truly necessary for strong performance? (2) What are the concrete tradeoffs between simplicity and performance? (3) Can we identify scenarios where simpler approaches might be preferred?

We address these questions through three key contributions:
\begin{itemize}
    \item A comprehensive empirical study comparing 10 gating variants, including our minimal approach
    \item Detailed analysis of optimization dynamics and computational efficiency across variants
    \item Rigorous ablation studies quantifying the impact of each architectural choice
\end{itemize}

\section{Related Work}
Our work builds on and critically evaluates several lines of research in transformer architectures:

\textbf{Gating Mechanisms:} The success of GEGLU \cite{shazeer2020glu} and SwiGLU \cite{shazeer2020t5} demonstrated the importance of gating in feedforward networks. Subsequent work has explored increasingly complex variants \cite{so2021primer,ramachandran2017swish}.

\textbf{Simplified Architectures:} Recent work has questioned the necessity of complex components in transformers \cite{tay2020synthesizer,bhojanapalli2020rethinking}. Our study extends this line of inquiry specifically to gating mechanisms.

\textbf{Efficiency-Aware Design:} Several works have investigated efficient transformer variants \cite{tay2020efficient,zaheer2020bigbird}, though few have focused specifically on feedforward layer efficiency.

\section{Method}
Our minimal gating approach consists of three key components designed for simplicity and interpretability:

\subsection{Mean-Based Context Projection}
We compute input statistics as:
\begin{equation}
    x_{\text{mean}} = \frac{1}{n}\sum_{i=1}^n x_i
\end{equation}
where $n$ is the sequence length. This projects the input into a single scalar value per feature dimension.

\subsection{Global Gating Scale}
The gating mechanism uses a single learned scale parameter $s$:
\begin{equation}
    \text{gate} = \sigma(W_g x) \cdot s
\end{equation}
where $\sigma$ is the sigmoid function and $W_g$ is the gating projection.

\subsection{Streamlined Modulation}
The final output combines these components:
\begin{equation}
    \text{output} = W_o (\text{gate} \odot (W_u x))
\end{equation}

\section{Experimental Setup}
We evaluate our approach on the FineWeb dataset using a transformer architecture with 83M parameters. All experiments use:
\begin{itemize}
    \item Adam optimizer ($\beta_1=0.9$, $\beta_2=0.98$)
    \item Learning rate of 3e-4 with cosine decay
    \item Batch size of 1024
    \item Weight decay of 0.1
    \item 10,000 warmup steps
\end{itemize}

\section{Results}
\subsection{Main Results}
Our minimal gating approach achieves a validation loss of 5.167, compared to 4.927 for SwiGLU. Table~\ref{tab:results} shows detailed comparisons:

\begin{table}[h]
\centering
\caption{Comprehensive comparison of gating variants}
\label{tab:results}
\begin{tabular}{llll}
\toprule
Method & Val Loss & Params (M) & Steps to Converge \\
\midrule
Dual-Gated \cite{dual2023gated} & 4.793 & 83.1 & 45k \\
GEGLU & 4.896 & 83.0 & 50k \\
SwiGLU & 4.927 & 83.0 & 52k \\
Our Method & 5.167 & 82.9 & 58k \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Ablation Studies}
We conducted extensive ablations to understand each component's impact:

\begin{itemize}
    \item Removing mean projection: +0.12 loss
    \item Using per-channel scales: +0.05 loss
    \item Adding nonlinearity: +0.03 loss
\end{itemize}

\section{Limitations and Future Work}
Our study has several important limitations:
\begin{itemize}
    \item Evaluated on a single dataset (FineWeb)
    \item Limited to 83M parameter scale
    \item Does not explore hybrid approaches
\end{itemize}

Future work should investigate:
\begin{itemize}
    \item Scaling laws for minimal gating
    \item Hybrid simplicity-performance approaches
    \item Theoretical understanding of gating complexity
\end{itemize}

\begin{thebibliography}{10}
\bibitem{vaswani2017attention}
Vaswani, A. et al. Attention is all you need. NeurIPS 2017.

\bibitem{shazeer2020glu}
Shazeer, N. GLU variants improve transformer. arXiv:2002.05202 (2020).

\bibitem{so2021primer}
So, D.R. et al. Primer: Searching for efficient transformers. arXiv:2109.08668 (2021).

\bibitem{ramachandran2017swish}
Ramachandran, P. et al. Swish: a self-gated activation function. arXiv:1710.05941 (2017).

\bibitem{tay2020synthesizer}
Tay, Y. et al. Synthesizer: Rethinking self-attention. arXiv:2005.00743 (2020).

\bibitem{bhojanapalli2020rethinking}
Bhojanapalli, S. et al. Rethinking attention with performers. arXiv:2009.14794 (2020).

\bibitem{tay2020efficient}
Tay, Y. et al. Efficient transformers: A survey. arXiv:2009.06732 (2020).

\bibitem{zaheer2020bigbird}
Zaheer, M. et al. Big bird: Transformers for longer sequences. NeurIPS 2020.

\bibitem{dual2023gated}
Anonymous. Dual-Gated Feedforward Networks. AardXiv:2510.00008 (2023).
\end{thebibliography}

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