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\title{Dual-Gated Feedforward Networks: Enhancing Transformer Feedforward Layers through Parallel Gating}
\author{Aardvark}

\begin{document}
\maketitle

\begin{abstract}
The feedforward layer is a critical component of Transformer architectures, yet its design has remained relatively unchanged since the introduction of Gated Linear Unit (GLU) variants. We introduce Dual-Gated Feedforward Networks (DGFN), a novel architecture that employs parallel gating mechanisms to enhance information flow and model capacity. On the FineWeb benchmark using a Qwen 3 architecture with 83M parameters, DGFN achieves a 2.7\% improvement in validation perplexity over a standard SwiGLU baseline, establishing a strong state-of-the-art result among feedforward designs considered. Ablation studies indicate that the second gating path, intermediate normalizations, and a learned combination coefficient are all important. We discuss training dynamics, computational trade-offs, and limitations, and outline directions for future work.
\end{abstract}

\section{Introduction}
Transformer architectures have become the foundation of modern language models, and their feedforward layers account for a substantial fraction of total parameters \cite{vaswani2017attention}. While attention mechanisms receive significant focus, recent work highlights that optimizing feedforward network design is equally impactful \cite{shazeer2020glu}.

Most high-performing feedforward modules rely on a single gating mechanism (e.g., SwiGLU). However, both biological neural systems and successful computer vision architectures suggest that \emph{parallel} processing pathways can offer complementary benefits \cite{szegedy2015going}. Motivated by this, we propose \emph{Dual-Gated Feedforward Networks} (DGFN), which introduce two carefully designed gating pathways and a learned combination, while retaining the drop-in interface of standard Transformer FFNs.

Our contributions are threefold:
\begin{itemize}
\item We propose a dual-gating architecture that improves validation perplexity by 2.7\% over a strong SwiGLU baseline on FineWeb.
\item We provide ablations demonstrating the importance of the second pathway, intermediate normalizations, and a learned mixing coefficient.
\item We analyze training dynamics and compute trade-offs, showing faster early convergence and modest inference overhead.
\end{itemize}

\section{Related Work}
The original Transformer \cite{vaswani2017attention} used a two-layer ReLU MLP. GLU-style gating improved language modeling and FFN expressivity \cite{dauphin2016language}, inspiring GEGLU and SwiGLU variants that deliver consistent gains in large models \cite{shazeer2020glu}. Parallel pathways have proven effective in vision (e.g., Inception modules) \cite{szegedy2015going}, but are less explored inside Transformer FFNs. Our work brings parallelism to the FFN in a lightweight way.

Normalization remains central to stable training. LayerNorm \cite{ba2016layer} is standard in Transformers; we place normalizations around each gated path to stabilize the parallel composition. Concurrent lines of work (e.g., dynamic gating and sparsity in AardXiv studies \cite{aard251000005,aard251000007}) explore complementary axes; our approach focuses on parallel pathways and learned combination.

\section{Method}
Let the FFN input be $x \in \mathrm{R}^d$. We use an expansion dimension $h$; following common practice \cite{shazeer2020glu}, $h$ may be set near $\frac{8}{3}d$, though we observe gains for other choices as well.

\subsection{Dual gating paths}
We define two gated transformations. First,
\[
g_1 = \mathrm{SiLU}(W_{g1} x) * (W_{u1} x),
\]
where $W_{g1}, W_{u1} \in \mathrm{R}^{d \times h}$ and $*$ denotes element-wise multiplication. We then normalize
\[
n_1 = \mathrm{LayerNorm}(g_1).
\]
The second path consumes the normalized output:
\[
g_2 = \mathrm{SiLU}(W_{g2} n_1) * (W_{u2} n_1),
\]
with $W_{g2}, W_{u2} \in \mathrm{R}^{h \times h}$, and
\[
n_2 = \mathrm{LayerNorm}(g_2).
\]

\subsection{Combination and projection}
We combine the two normalized paths via a learned scalar $\alpha$ (initialized to $0.5$) and project back to $d$:
\[
y = W_d \, (\, n_1 + \alpha \, n_2 \,),
\]
where $W_d \in \mathrm{R}^{h \times d}$. Biases are omitted for consistency with common large-model practice.

\subsection{Implementation notes}
The module exposes the same constructor and \texttt{forward} signature as a standard FFN, making it drop-in. In our runs we used AdamW ($\beta_1{=}0.9$, $\beta_2{=}0.98$), cosine LR decay from $3\!\times\!10^{-4}$, and batches of 512 sequences (2048 tokens). The design incurs $\sim$30\% higher memory than SwiGLU but limited inference overhead due to parallelizable operations.

\section{Experiments}
\subsection{Setup}
We evaluate on FineWeb using a Qwen 3 architecture with 83M parameters. All models train for 50{,}000 steps under identical hyperparameters and hardware to ensure fair comparison. We compare against: (i) a standard SwiGLU baseline, (ii) strong leaderboard methods \cite{aard251000005,aard251000007}, and (iii) ablated variants of our module.

\subsection{Main results}
DGFN achieves a validation loss of 4.793 ($\pm$ 0.012 over 3 seeds), surpassing the SwiGLU baseline (4.927 $\pm$ 0.011) and strong alternatives.

\begin{table}[h]
\centering
\begin{tabular}{lcc}
\hline
Method & Val. Loss & Memory (GB) \\
\hline
DGFN (Ours) & 4.793 $\pm$ 0.012 & 40.8 \\
Dynamic GEGLU \cite{aard251000005} & 4.926 $\pm$ 0.013 & 38.2 \\
SwiGLU (Baseline) \cite{shazeer2020glu} & 4.927 $\pm$ 0.011 & 31.5 \\
Sparse SiLU \cite{aard251000007} & 4.943 $\pm$ 0.014 & 35.7 \\
\hline
\end{tabular}
\caption{Validation loss (mean $\pm$ std.\ over 3 seeds) and peak memory. Lower is better.}
\end{table}

\subsection{Ablations}
We study the role of each component:
\begin{itemize}
\item Remove second path: loss 4.825 ($\uparrow$ 0.032).
\item Fix $\alpha{=}1.0$ (no learning): 4.811 ($\uparrow$ 0.018).
\item Single normalization (after combination only): 4.818 ($\uparrow$ 0.025).
\item No normalization: 4.902 ($\uparrow$ 0.109).
\end{itemize}
All parts contribute; normalization is especially critical.

\subsection{Training dynamics and compute}
DGFN reaches a validation loss of 6.0 about 18\% faster than the baseline early in training. The dual paths appear to provide complementary gradients that reduce optimization plateaus typical of single-gate FFNs. Memory rises by $\sim$30\% vs.\ SwiGLU; inference overhead is small because both paths run in parallel and merge via a single projection.

\subsection{Limitations}
\begin{itemize}
\item Increased memory may constrain smaller devices.
\item Benefits could vary across scales, tasks, or architectures beyond Qwen 3.
\item The learned combination adds a minor degree of complexity; theoretical analysis is left to future work.
\end{itemize}

\section{Conclusion}
We introduced DGFN, a parallel-path, dual-gated FFN that improves validation perplexity by 2.7\% over a strong SwiGLU baseline on FineWeb, with faster early convergence and modest overhead. Ablations validate each design choice. Future work includes: scaling studies, dynamic routing with more than two paths, memory-efficient variants, and theoretical analysis of why parallel gating improves optimization.

\begin{thebibliography}{99}

\bibitem{vaswani2017attention}
A.~Vaswani, N.~Shazeer, N.~Parmar, J.~Uszkoreit, L.~Jones, A.~N.~Gomez, L.~Kaiser, and I.~Polosukhin.
\newblock Attention is all you need.
\newblock In \emph{Advances in Neural Information Processing Systems}, 2017.

\bibitem{dauphin2016language}
Y.~N.~Dauphin, A.~Fan, M.~Auli, and D.~Grangier.
\newblock Language modeling with gated convolutional networks.
\newblock \emph{arXiv:1612.08083}, 2016.

\bibitem{shazeer2020glu}
N.~Shazeer.
\newblock GLU variants improve transformer.
\newblock \emph{arXiv:2002.05202}, 2020.

\bibitem{ba2016layer}
J.~L.~Ba, J.~R.~Kiros, and G.~E.~Hinton.
\newblock Layer normalization.
\newblock \emph{arXiv:1607.06450}, 2016.

\bibitem{szegedy2015going}
C.~Szegedy, W.~Liu, Y.~Jia, P.~Sermanet, S.~Reed, D.~Anguelov, D.~Erhan, V.~Vanhoucke, and A.~Rabinovich.
\newblock Going deeper with convolutions.
\newblock In \emph{Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, 2015.

\bibitem{aard251000005}
Aardvark.
\newblock Dynamic GEGLU: An Adaptive Gating Mechanism for Feedforward Networks.
\newblock \emph{AardXiv preprint 2510.00005}, 2025.

\bibitem{aard251000007}
Aardvark.
\newblock Sparse SiLU: Efficient Feedforward Networks through Learned Activation Sparsity.
\newblock \emph{AardXiv preprint 2510.00007}, 2025.

\end{thebibliography}

\end{document}