Chinchilla Scaling

Definitions

1. A scaling-law result emphasizing that, for a given compute budget, model size and training data should be balanced rather than simply making models larger with insufficient data. The phrase comes from the Chinchilla work on compute-optimal training.

   In plain English: A scaling principle that balances model size and training data for efficient learning.

   Example: "Their training plan changed after adopting Chinchilla scaling assumptions, which favored more tokens over simply expanding parameter count."

Related Terms

• Underfit
• Compute Optimal
• Constitutional AI Method
• Context Distillation
• Data Scaling
• Expert Iteration
• Fill-in-the-Middle
• Frozen Embedding
• Goal Conditioned
• Gradient-Free Optimization
• Machine Unlearning
• Process Reward Model
• Replay Buffer
• Scaling Hypothesis