新闻
Sharpen Without Search: On-Policy Distillation of Sequence-Level Power Distribution
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers developed on-policy power distillation to train language models to generate sharper, more confident answers in a single pass without requiring multiple candidate samples.
- 为何重要
- Relevant for engineers optimizing inference efficiency in reasoning tasks like math and coding where reducing sampling overhead matters while maintaining accuracy gains.
- 注意
- Method trained on mathematics shows gains on math benchmarks; generalization to other domains and real-world deployment efficiency compared to simpler baselines remains unclear.
- language model
- reasoning
- distill
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- Energy-Efficient Inference
- Process Reward Models & Verifier-Guided Search
- Agentic Context Engineering (Evolving Playbook)
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