Spectral Adaptation in Transformer Optimization: A Detailed Empirical Study

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Abstract:We present a comprehensive investigation of spectral adaptation techniques for transformer optimization. Building on established optimization frameworks, we introduce layer-specific spectral analysis to adapt learning rates based on gradient matrix properties. Our experiments demonstrate modest improvements over AdamW (5.211 vs 4.927 validation loss), though falling short of state-of-the-art approaches.