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cloud.google.com·7h ago
AI Research
arXiv CS.AI·27d ago·5 min read
One Spectrum, Two Resources: Data-Memory Scaling in Autoregressive Prediction
TL;DR
A new framework reveals how learned memory and data interact in autoregressive prediction, governed by a predictive-energy spectrum. Key findings include a minimax law that connects data resolution and memory allocation, enhancing model efficiency.
✦ Why It Matters
Implement the proposed minimax law to optimize memory allocation in your autoregressive models today.
Key Takeaways
- Explore the predictive-energy spectrum for better memory allocation.
- Implement masked query-key attention for improved routing in models.
- Validate data-memory collapse in your existing models.
- Optimize memory strategies based on the minimax law findings.
How It Works
The framework connects learned memory and data through a predictive-energy spectrum, where each coordinate's contribution is determined by its query probability and the squared radius of its unknown logit. The minimax law derived from this spectrum provides a mathematical foundation for understanding how to allocate memory efficiently relative to the amount of data.