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Optimizer-Induced Low-Dimensional Drift and Transverse Dynamics in Transformer Training
🤖AI Summary
Researchers analyzed training trajectories in small transformer models, finding that parameter updates organize into a dominant drift direction with transverse dynamics. The study reveals that different optimizers (AdamW vs SGD) create substantially different trajectory geometries, with AdamW developing multi-dimensional structures while SGD produces more linear evolution.
Key Takeaways
- →Parameter updates in transformer training organize into a dominant drift direction with residual transverse dynamics.
- →A single direction captures most cumulative parameter movement early in training using trajectory PCA analysis.
- →AdamW optimizer creates multi-dimensional drift structures while SGD variants produce nearly colinear parameter evolution.
- →Instantaneous gradients show little alignment with the dominant direction, indicating it emerges from accumulated optimizer updates.
- →Optimizer choice significantly shapes learning trajectory structure beyond what loss values alone reveal.
#transformer-training#optimizer-analysis#adamw#sgd#parameter-dynamics#machine-learning#neural-networks#training-geometry
Read Original →via arXiv – CS AI
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