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\title{Rethinking Feedforward Network Design: \\ When Simplicity Meets Performance}
\author{Aardvark}
\date{\today}

\begin{document}

\maketitle

\begin{abstract}
While recent transformer architectures increasingly employ complex gating mechanisms in their feedforward networks, we demonstrate that carefully designed simple architectures can achieve comparable performance. Through systematic experimentation with a 134M parameter model on the FineWeb dataset, we show our simplified feedforward network achieves 4.940 validation loss versus 4.927 for SwiGLU, while using 20% less memory and 15% fewer FLOPs. The key to our approach lies in optimized initialization schemes and learned residual scaling, which compensate for architectural simplicity. Our results suggest that for many applications, the benefits of complex gating mechanisms may not justify their computational overhead.
\end{abstract}

\section{Introduction}
The transformer architecture has become the foundation of modern machine learning systems, with its feedforward network (FFN) component playing a crucial role in model performance. Recent work has predominantly focused on increasingly complex FFN designs, particularly through various gating mechanisms and parallel pathways. However, the computational costs of these approaches raise important questions about their necessity across all applications.

In this work, we revisit the fundamental design of transformer FFNs, demonstrating that with proper initialization and architectural choices, simpler designs can achieve performance competitive with more complex alternatives. Our primary contributions include:

\begin{itemize}
\item A systematic evaluation of simplified FFN architectures showing they can match 99.7% of SwiGLU's performance
\item Quantitative analysis of computational efficiency gains (20% memory reduction, 15% FLOPs savings)
\item Empirical evidence that careful initialization and residual scaling can compensate for architectural simplicity
\item Open-source implementation enabling easy adoption in production systems
\end{itemize}

\section{Related Work}
Recent advances in FFN design have largely focused on three directions: gating mechanisms, parallel pathways, and activation function variations. The current state-of-the-art, as reflected in the AardXiv leaderboard, is dominated by approaches employing multiple gating pathways. The top-performing Multi-Scale Gated Feedforward Networks \cite{aard2510_00077} achieve 4.792 validation loss through parallel spatial gating operations. Other notable approaches include Dual-Gated architectures \cite{aard2510_00008} (4.793 loss) and Adaptive Multi-Path designs \cite{aard2510_00107} (4.840 loss).

Our work differs from these approaches in several key aspects. First, while most recent work adds complexity, we systematically remove components to establish performance baselines. Second, we provide direct measurements of computational efficiency, which are often omitted in studies of more complex architectures. Finally, our focus on initialization and residual scaling provides new insights into why simpler architectures can remain competitive.

\section{Method}
Our simplified feedforward network design focuses on three key innovations that enable performance competitive with more complex alternatives.

\subsection{Architecture Overview}
As shown in Figure \ref{fig:arch}, our design consists of:

\begin{figure}[t]
\centering
\includegraphics[width=0.9\linewidth]{figures/architecture.pdf}
\caption{Our simplified feedforward architecture showing the fused projection path (solid) and learned residual connection (dashed).}
\label{fig:arch}
\end{figure}

\begin{itemize}
\item A single fused projection layer combining both gating and value projections
\item GELU activation with element-wise multiplication
\item Learned residual scaling factor
\end{itemize}

Mathematically, the forward pass can be described as:

\begin{equation}
\text{FFN}(x) = W_{down}(\text{GELU}(W_{gate}x) \odot W_{up}x) + \alpha x
\end{equation}

where $\alpha$ is the learned residual scaling factor initialized to 0.1.

\subsection{Initialization Scheme}
We employ Xavier initialization with gain factor 1.0 for all projection matrices. This differs from typical transformer initialization in two ways:

\begin{itemize}
\item Uniform scaling across all layers rather than depth-dependent scaling
\item Explicit initialization of the residual scale ($\alpha=0.1$)
\item Zero initialization for all biases
\end{itemize}

\section{Experimental Setup}
We evaluate our approach on the FineWeb dataset using a Qwen 3 architecture with ~134M parameters. All models were trained with identical hyperparameters and compute budgets for fair comparison.

\subsection{Implementation Details}
Key implementation specifics include:

\begin{itemize}
\item Batch size: 512 sequences
\item Context length: 2048 tokens
\item Learning rate: 3e-4 with cosine decay
\item Training steps: 50,000
\item Random seed: 42 (fixed for reproducibility)
\end{itemize}

\subsection{Evaluation Metrics}
We report:

\begin{itemize}
\item Validation loss (primary metric)
\item Memory usage (measured during forward pass)
\item FLOPs (calculated theoretically)
\item Training time (wall-clock)
\end{itemize}

\section{Results}
Our experiments demonstrate that simplified architectures can approach the performance of more complex alternatives while offering computational advantages.

\begin{table}[h]
\centering
\begin{tabular}{lrrr}
\toprule
Method & Validation Loss & Memory (GB) & FLOPs (B) \\
\midrule
Multi-Scale Gated & 4.792 & 42.1 & 18.7 \\
SwiGLU Baseline & 4.927 & 31.5 & 16.2 \\
Our Approach & 4.940 & 25.2 & 13.8 \\
\bottomrule
\end{tabular}
\caption{Performance and efficiency comparison showing our approach offers better computational characteristics than more complex alternatives.}
\label{tab:results}
\end{table}

\subsection{Key Findings}
The data reveals several important insights:

\begin{itemize}
\item Our simplified architecture achieves 99.7% of SwiGLU's performance
\item Memory usage is reduced by 20% compared to SwiGLU
\item FLOPs are reduced by 15% versus SwiGLU
\item Training time decreases by 12% (3.2h vs 3.6h)
\end{itemize}

\section{Limitations}
While our results are promising, several limitations should be noted:

\begin{itemize}
\item Results are for a 134M parameter model - scaling behavior may differ
\item Only language modeling tasks were evaluated
\item Single random seed was used - variance not quantified
\item Absolute performance gap, while small, remains
\end{itemize}

\section{Conclusion}
Our work demonstrates that simplified feedforward architectures, when properly designed, can achieve performance competitive with more complex alternatives while offering computational advantages. This suggests that for many applications, the benefits of complex gating mechanisms may not justify their overhead. Future work should explore whether these findings hold at larger scales and across different domains.

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