新闻
Train Where the Quantized Model Goes: On-Policy Distillation for Low-Bit Reasoning
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers introduced on-policy distillation to fix reasoning failures in heavily quantized language models, improving math and code performance from 35% to 70% retention.
- 为何重要
- Engineers deploying sub-3-bit quantized models for inference need this when reasoning tasks degrade into repetitive loops and incomplete solutions.
- 注意
- The method requires a frozen full-precision teacher model during training, adding computational overhead; gains are specific to reasoning tasks, not general QA.
- reasoning
- distill
- quantiz
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