Distillation Loss Trend 2026

Distillation loss is the error measured when a smaller "student" model mimics a larger "teacher" model’s outputs, typically using soft probabilities. It drives knowledge distillation, transferring nuanced insights beyond hard labels. This technique benefits AI teams deploying efficient models on edge devices, reducing computational costs while retaining high accuracy, and is crucial for real-time applications.