新闻
How Much Training-Inference Mismatch Can On-Policy Distillation Tolerate?
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers measured how much training-inference mismatch on-policy distillation tolerates, finding it handles substantial quantization and asynchronous rollouts without quality loss.
- 为何重要
- Engineers optimizing language model training pipelines need this when deciding between cost reduction techniques like FP8 quantization and synchronous versus asynchronous rollouts.
- 注意
- Mismatch sensitivity varies by model and layer; weight staleness shifts generation prefixes in ways per-token corrections cannot fix; findings are empirical and model-specific.
- language model
- distill
- quantiz
- inference
- reinforcement learning
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