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\title{Revisiting AdamW: A Comprehensive Evaluation of Optimizer Modifications for Transformer Language Models}
\author{Aardvark}

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\maketitle

\begin{abstract}
This paper presents a systematic evaluation of optimizer modifications for transformer language models, comparing them against the standard AdamW implementation. Through extensive experimentation with momentum scaling, parameter grouping, and learning rate adaptation techniques on a 134M parameter Qwen model trained on the FineWeb dataset, we find that AdamW remains remarkably robust. Our results demonstrate that several intuitive modifications either failed to improve performance or degraded training stability. While our attempts to innovate on the optimizer did not yield improvements, the negative results provide valuable insights into optimizer design for large language models and suggest that research efforts might be better directed toward other aspects of model training.
\end{abstract}

\section{Introduction}
The optimization of transformer language models presents unique challenges due to their scale and complex loss landscapes. While numerous optimizer variants have been proposed \cite{kingma2014adam,reddi2018convergence,loshchilov2017decoupled}, AdamW remains the de facto standard in practice. This work investigates whether careful modifications to AdamW could yield improvements in training efficiency or final model performance.

Our investigation was motivated by several recent papers proposing optimizer innovations \cite{orthoadaptive,stableadam,simpleadaptive}. However, as we demonstrate through rigorous experimentation, many promising modifications fail to deliver consistent improvements over the baseline AdamW implementation. Our work contributes to a growing body of literature suggesting that simple, well-tuned optimizers often outperform more complex variants \cite{simpleadaptive}.

\section{Related Work}
Recent work on optimizers for large language models has explored various directions. The adaptive moment estimation (Adam) optimizer \cite{kingma2014adam} introduced individual adaptive learning rates for parameters. Subsequent work identified issues with Adam's weight decay implementation \cite{loshchilov2017decoupled}, leading to AdamW. More recent innovations include:

\begin{itemize}
\item Orthogonal gradient processing \cite{orthoadaptive}
\item Layer-wise adaptation \cite{simpleadaptive}
\item Memory-efficient variants \cite{shazeer2018memory}
\end{itemize}

While these approaches show promise, they often introduce significant complexity. Our work provides empirical evidence that simpler approaches may be preferable for many applications.

\section{Methods}
We conducted a series of ablation studies comparing various AdamW modifications against the baseline implementation. Our experimental protocol used:

\begin{itemize}
\item Model: Qwen architecture with 134M parameters
\item Dataset: FineWeb benchmark (2.9B tokens)
\item Training: Chinchilla-optimal token count (1.67B tokens)
\item Batch size: 4,194,304 tokens
\item Gradient accumulation: 16 steps
\item Evaluation: Validation perplexity
\end{itemize}

We evaluated four primary modifications, each carefully tuned:

\begin{enumerate}
\item Multi-timescale momentum adaptation (betas: 0.9, 0.95, 0.99)
\item Layer-specific learning rate scaling (attention layers scaled by 1.2x)
\item Parameter-group aware weight decay (0.1 for weights, 0.0 for biases)
\item Combined momentum-spectral approaches
\end{enumerate}

All experiments used the same random seed and were run on identical hardware to ensure fair comparison.

\section{Results}
As shown in Table~\ref{tab:results}, none of our modifications improved upon the AdamW baseline:

\begin{table}[h]
\centering
\caption{Validation Loss Comparison}
\label{tab:results}
\begin{tabular}{ll}
\toprule
Method & Validation Loss \\
\midrule
AdamW Baseline & 4.927 \\
Our Final Implementation & 4.954 \\
Multi-Momentum & 11.500 \\
Layer-Specific LR & 11.672 \\
Grouped Weight Decay & 11.812 \\
Combined Approach & 11.937 \\
\bottomrule
\end{tabular}
\end{table}

These results suggest that AdamW's default configuration is remarkably well-tuned for transformer language models. The performance degradation from our modifications indicates that careful parameter grouping and momentum handling are crucial for stable training.

\section{Limitations}
Our study has several important limitations:

\begin{itemize}
\item Evaluated on a single model size (134M parameters)
\item Tested on one dataset (FineWeb)
\item Limited hyperparameter search for each variant
\item No downstream task evaluation
\end{itemize}

Future work should validate these findings across different model scales and datasets.

\section{Conclusion}
Our comprehensive evaluation demonstrates that AdamW remains a robust choice for transformer language model optimization. While the negative results may seem disappointing, they provide valuable insights:

\begin{itemize}
\item Simple, well-tuned optimizers can outperform more complex variants
\item AdamW's default parameters work well for transformer architectures
\item Future work should focus on orthogonal improvements rather than AdamW modifications
\end{itemize}

These findings suggest that research efforts might be better directed toward areas like curriculum learning or architecture modifications rather than optimizer innovations.

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