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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Jul 27·5 min read
Hyperball May Not Be a Free Lunch
TL;DR
Hyperball-style optimizers enhance large-scale training in deep networks by normalizing updates, but their advantages are not fully understood. This study reveals that effective learning rates and careful learning-rate scheduling are crucial for optimizing performance.
✦ Why It Matters
Evaluate and adjust your learning-rate schedule to optimize training performance with Hyperball-style optimizers.
Key Takeaways
- Understand the role of angular effective learning rates in optimizer performance.
- Experiment with learning-rate schedules to enhance training outcomes.
- Recognize that radial updates have limited impact on learning rates.
- Monitor the evolution of effective step sizes during training.
How It Works
The study derives an angular effective learning rate by analyzing the angular displacement between consecutive parameter states, incorporating the parameter-update angle, parameter norm, and update norm. By decomposing updates into radial and tangential components, it reveals that radial updates do not significantly influence the angular effective learning rate, challenging previous assumptions about their effectiveness.