Train Where the Quantized Model Goes: On-Policy Distillation for Low-Bit Reasoning

In 30 Sekunden

Was passiert ist

Researchers introduced on-policy distillation to fix reasoning failures in heavily quantized language models, improving math and code performance from 35% to 70% retention.

Warum es zählt

Engineers deploying sub-3-bit quantized models for inference need this when reasoning tasks degrade into repetitive loops and incomplete solutions.

Achtung

The method requires a frozen full-precision teacher model during training, adding computational overhead; gains are specific to reasoning tasks, not general QA.