新闻
Learning to Stop without Learning to Stop: Self-Supervised Confidence Training Improves Reasoning Efficiency
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers show that training reasoning models to predict confidence in answers reduces token generation by up to 25% without explicit stopping mechanisms or length penalties.
- 为何重要
- Engineers building or deploying reasoning models care when inference cost and latency matter more than squeezing maximum accuracy from every query.
- 注意
- The method was tested on math, science, and coding tasks with only 600 training problems. Generalization to other domains and scalability remain unclear.
- reasoning
- rag
- fine-tun
- inference
- reinforcement learning
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