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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Jul 27·5 min read
Hidden Failure Modes of Gradient Modification under Adam in Continual Learning, and Adaptive Decoupled Moment Routing as a Repair
TL;DR
Gradient modifications in continual learning using Adam can lead to hidden failure modes, causing performance collapse. Adaptive decoupled moment routing effectively mitigates this issue, improving stability and performance metrics significantly.
✦ Why It Matters
Implement adaptive decoupled moment routing to enhance stability in continual learning applications using Adam.
Key Takeaways
- Use adaptive decoupled moment routing to prevent performance collapse.
- Be aware of hidden failure modes in gradient modifications with Adam.
- Monitor effective learning rates when applying gradient modifications.
- Test performance across multiple domains to ensure stability.
How It Works
The proposed adaptive decoupled moment routing modifies gradients by routing changes only to the first moment, which helps maintain the integrity of second-moment statistics. This approach prevents the inflation of the effective learning rate that typically occurs with traditional gradient modifications, thus stabilizing performance in continual learning tasks.