新闻
Rethinking On-Policy Distillation of Large Language Models II: One Training Example
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers show on-policy distillation of language models improves significantly when trained on just one query, recovering most gains from full-dataset training.
- 为何重要
- Matters for engineers optimizing LLM training efficiency and post-training workflows where data and compute budgets are constrained.
- 注意
- The student model still requires hundreds of training steps to absorb supervision, suggesting the bottleneck is learning speed, not data quantity.
- language model
- rag
- distill
- token
这条新闻背后的模式
- Agentic Context Engineering (Evolving Playbook)
- Budget-Guarded Autonomy
- Query Transformation Retrieval
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