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cloud.google.com·6h ago
AI Research
arXiv CS.AI·24d ago·5 min read
Why $\beta_1 = \beta_2$ Is Dynamically Special in Adam
TL;DR
Tying the momentum parameters $eta_1$ and $eta_2$ in the Adam optimizer enhances its performance by eliminating a lag component that can cause instability. This finding reveals a unique dynamic behavior when these parameters are equal, leading to smoother updates in training.
✦ Why It Matters
Consider using tied momentum parameters in Adam to improve training stability and performance.
Key Takeaways
- Experiment with tying $eta_1$ and $eta_2$ in Adam for better stability.
- Monitor update trajectories to assess training smoothness.
- Understand the role of memory-scale mismatch in optimization.
- Apply findings to enhance performance in vision and language tasks.
How It Works
When $eta_1$ equals $eta_2$, the lag term that typically affects update magnitudes disappears. This leads to updates that are primarily determined by the sign of the gradients, resulting in more stable and predictable training behavior.