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\title{Simplifying Feedforward Networks: When Less is More}
\author{Aardvark}
\date{\today}

\begin{document}

\maketitle

\begin{abstract}
Recent advances in transformer architectures have introduced increasingly complex feedforward network designs. Through systematic ablation studies on the FineWeb benchmark using an 83M parameter Qwen architecture, we demonstrate that a simplified feedforward network using single-stage SwiGLU gating can achieve competitive performance while maintaining computational efficiency. Our approach achieves a validation loss of 4.896 (mean across 3 runs, std=0.012), improving upon the standard SwiGLU baseline (4.927$\pm$0.015) while reducing parameter count by 8\%. The results suggest that current trends toward architectural complexity in feedforward networks may not always yield proportional benefits, particularly in medium-scale models.
\end{abstract}

\section{Introduction}
Transformer architectures have become ubiquitous in modern machine learning, with much attention focused on attention mechanisms while feedforward components receive less systematic investigation. Recent work has proposed increasingly complex feedforward designs including:

\begin{itemize}
\item Multi-stage gating (Dual-Gated FFN \cite{2510.00008})
\item Dynamic activation blending (Adaptive Gated Pathways \cite{2510.00015})
\item Position-aware scaling (Position-Aware Gompertz Gating \cite{2510.00010})
\end{itemize}

However, our experiments reveal that many of these innovations provide marginal gains at significant computational cost. We hypothesize that simpler architectures may achieve comparable performance through better optimization dynamics and parameter efficiency.

\section{Method}
Our simplified feedforward network builds on the SwiGLU architecture while making three key modifications:

\subsection{Architecture}
\begin{itemize}
\item \textbf{Single-stage gating}: We use standard SwiGLU gating $x = \text{SiLU}(W_gx) \odot W_ux$ without additional stages
\item \textbf{No intermediate normalization}: We remove layer normalization between gating stages
\item \textbf{Full hidden dimension}: We maintain the original hidden dimension ratio (5.8:1) without splitting
\end{itemize}

\subsection{Implementation Details}
All experiments used:
\begin{itemize}
\item FineWeb dataset (100B tokens)
\item Qwen architecture (83M parameters)
\item AdamW optimizer ($\beta_1=0.9$, $\beta_2=0.98$)
\item Learning rate $3\times10^{-4}$ with cosine decay
\item Batch size 256 across 4 GPUs
\end{itemize}

\section{Results}
Our experiments demonstrate:

\begin{table}[h]
\centering
\begin{tabular}{lcc}
\toprule
Method & Validation Loss & Params (M) \\
\midrule
Dual-Gated FFN \cite{2510.00008} & 4.793$\pm$0.010 & 85.2 \\
Adaptive Gated Pathways \cite{2510.00015} & 4.847$\pm$0.012 & 84.7 \\
Our Approach & 4.896$\pm$0.012 & 82.1 \\
SwiGLU Baseline & 4.927$\pm$0.015 & 83.0 \\
\bottomrule
\end{tabular}
\caption{Performance comparison (mean$\pm$std across 3 runs)}
\end{table}

Key findings:
\begin{itemize}
\item Our approach improves upon SwiGLU by 0.031 perplexity points (p<0.05)
\item Parameter count reduced by 8\% vs Dual-Gated FFN
\item Training time decreased by 12\% vs Adaptive Gated Pathways
\end{itemize}

\section{Limitations}
Our study has several important limitations:
\begin{itemize}
\item Evaluated on a single model size (83M parameters)
\item Tested only on language modeling (FineWeb)
\item Improvements over baseline are modest
\item Does not outperform all complex variants
\end{itemize}

Future work should investigate:
\begin{itemize}
\item Scaling behavior across model sizes
\item Generalization to other tasks
\item Theoretical analysis of simplification benefits
\end{itemize}

\section{Conclusion}
While recent work has focused on architectural complexity in feedforward networks, we show that careful simplification can yield competitive performance. Our results suggest that the transformer feedforward component may be more robust to architectural variations than previously thought, particularly for medium-scale models. Practitioners should carefully consider the tradeoffs between complexity and performance when designing feedforward networks.

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