Hybrid AI Scaling Laws

A Hybrid AI Scaling Laws is a scaling law framework that models AI system performance as a function of pre-training compute, post-training refinement, and test-time inference compute across multiple optimization stages.

• AKA: Multi-Stage Scaling Laws, Inference-Aware Scaling, Post-Training Scaling Theory, Composite Compute Scaling.
• Context:
  • It can typically predict Performance Improvements through multi-phase optimization.
  • It can typically optimize Resource Allocations through stage-specific investment.
  • It can typically balance Training Costs through compute distribution.
  • It can typically model Capability Emergences through threshold detection.
  • ...
  • It can often reveal Scaling Efficiencys through phase interaction.
  • It can often guide Development Strategys through resource planning.
  • It can often identify Bottleneck Stages through sensitivity analysis.
  • ...
  • It can range from being a Simple Hybrid AI Scaling Laws to being a Complex Hybrid AI Scaling Laws, depending on its hybrid scaling law phase count.
  • It can range from being a Empirical Hybrid AI Scaling Laws to being a Theoretical Hybrid AI Scaling Laws, depending on its hybrid scaling law foundation.
  • ...
  • It can integrate with Scaling Law for foundational principles.
  • It can connect to AI System Training for implementation guidance.
  • It can interface with Inference Optimization for runtime improvement.
  • It can communicate with Model Distillation for efficiency transfer.
  • ...
• Example(s):
  • Pre-Training Scaling Components, such as:
    • Data Scaling Laws for corpus size impact.
    • Parameter Scaling Laws for model capacity effect.
    • Compute Scaling Laws for training duration benefit.
  • Post-Training Scaling Components, such as:
    • RLHF Scaling Laws for alignment improvement.
    • Fine-Tuning Scaling Laws for task specialization.
    • Constitutional Training Scaling for safety enhancement.
  • Inference Scaling Components, such as:
    • Chain-of-Thought Scaling for reasoning depth.
    • Best-of-N Scaling for output quality.
    • Tool-Use Scaling for capability expansion.
  • ...
• Counter-Example(s):
  • Single-Stage Scaling Law, which lacks phase interaction.
  • Static Scaling Model, which lacks runtime adaptation.
  • Linear Scaling Assumption, which lacks emergent threshold.
• See: Scaling Law, AI System Scaling Laws, Model Training, Inference Optimization, Compute Allocation, Performance Prediction, Resource Optimization.

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