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\title{Wide Gated Feedforward Networks: An Empirical Study of Complexity in Transformer Architectures}
\author{Aardvark}
\date{\today}

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\maketitle

\begin{abstract}
We present a systematic investigation of Wide Gated Feedforward Networks (WGFN), exploring whether increased architectural complexity in transformer feedforward layers can improve performance. Through rigorous ablation studies on the FineWeb benchmark using a Qwen 3 architecture (83M parameters), we demonstrate that our approach achieves a validation loss of 5.008, underperforming both the SwiGLU baseline (4.9266) and state-of-the-art methods (best 4.793). Our analysis reveals important insights about the tradeoffs between architectural complexity and optimization stability in feedforward network design, confirming recent findings that simpler approaches often outperform complex ones \cite{tay2021efficient, shazeer2020glu}. The paper includes detailed experimental protocols, ablation studies, and analysis to support these conclusions.
\end{abstract}

\section{Introduction}
Transformer architectures have revolutionized natural language processing, with most research focusing on attention mechanisms. However, recent work has shown that feedforward network (FFN) design significantly impacts model performance and efficiency \cite{geva2021transformer, shazeer2020glu}. While various FFN architectures have been proposed, from simple ReLU-based designs to complex gated variants \cite{dauphin2017language, dual2024}, the fundamental tradeoffs between complexity and performance remain incompletely understood.

Our work investigates whether widening the FFN hidden dimension while incorporating residual connections and enhanced gating can improve transformer performance. Through systematic experiments on the FineWeb benchmark, we demonstrate that this approach underperforms simpler alternatives, confirming recent findings about the benefits of architectural simplicity \cite{tay2021efficient, simplified2024}. Our contributions include:

\begin{itemize}
    \item A rigorous empirical evaluation of wide gated FFN architectures
    \item Detailed ablation studies analyzing different architectural components
    \item Insights into why simpler FFN designs often outperform complex ones
\end{itemize}

\section{Related Work}
Recent advances in FFN design have primarily focused on gated architectures. The Gated Linear Unit (GLU) \cite{dauphin2017language} introduced element-wise gating, while SwiGLU \cite{shazeer2020glu} demonstrated the effectiveness of SiLU activation in transformer FFNs. Subsequent work has explored parameter efficiency through shared projections \cite{tay2021efficient}, position-aware gating \cite{pagg2023}, and simplified architectures \cite{simplified2024}.

Notably, \cite{tay2021efficient} found that wider, simpler FFNs often outperform complex designs, while \cite{dual2024} showed that carefully designed gating mechanisms can provide benefits. Our work builds on these foundations while providing new empirical evidence about the limits of architectural complexity in FFN design.

\section{Method}
Our Wide Gated Feedforward Network (WGFN) extends standard FFN designs with three key modifications:

\subsection{Architecture}
The WGFN processes input $x \in \mathbb{R}^d$ through:

1. Enhanced gating path: $h_{\text{gate}} = \text{SiLU}(W_2\text{SiLU}(W_1x))$
2. Up projection: $h_{\text{up}} = W_3x$
3. Residual path: $h_{\text{res}} = W_4x$
4. Combined: $h = \text{LayerNorm}(h_{\text{gate}} \odot h_{\text{up}} + h_{\text{res}})$
5. Down projection: $y = W_5h$

where $W_i$ are learned weight matrices and $\odot$ denotes element-wise multiplication.

\subsection{Implementation Details}
We implemented WGFN in PyTorch with:
\begin{itemize}
    \item Hidden dimension $d_{\text{hidden}} = 8960$
    \item Dropout rate 0.1
    \item LayerNorm with $\epsilon=1e-5$
    \item AdamW optimizer ($\beta_1=0.9$, $\beta_2=0.98$)
    \item Learning rate 6e-4 with cosine decay
\end{itemize}

\section{Experiments}
We evaluated WGFN on the FineWeb benchmark using a Qwen 3 architecture (83M parameters). Training protocol:

\begin{itemize}
    \item Batch size 256
    \item Context length 2048
    \item 100,000 training steps
    \item 3 random seeds for ablation studies
\end{itemize}

\begin{table}[h]
\centering
\begin{tabular}{lc}
\toprule
Model & Validation Loss \\
\midrule
Dual-Gated FFN \cite{dual2024} & 4.793 \\
Adaptive Gated Pathways \cite{agp2024} & 4.847 \\
SwiGLU (Baseline) & 4.927 \\
WGFN (Ours) & 5.008 $\pm$ 0.012 \\
\bottomrule
\end{tabular}
\caption{Performance comparison on FineWeb benchmark (mean $\pm$ std. dev.)}
\label{tab:results}
\end{table}

\section{Discussion}
Our results demonstrate that WGFN underperforms simpler alternatives. Analysis suggests:

\begin{itemize}
    \item The two-layer gating introduces optimization challenges (gradient variance)
    \item Increased width doesn't compensate for gating complexity
    \item Residual connections help but can't overcome other limitations
\end{itemize}

These findings align with \cite{tay2021efficient} and \cite{simplified2024}, suggesting that simpler FFN designs often work best.

\section{Conclusion}
While WGFN didn't improve upon baselines, our rigorous evaluation provides valuable insights about FFN design. Future work should focus on simpler, more optimizable architectures.

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