📄 中文摘要
强化自我训练（ReST）是一种新方法，允许语言模型通过自身生成的例子进行自我学习，而无需每一步都依赖人工指导。该方法通过让模型生成大量句子并从中学习，使得相同的例子可以反复使用。由于训练使用的是存储的例子而非实时反馈，这种方法依赖于离线数据，从而节省了时间和计算资源，允许团队重用已有的工作。在翻译测试中，该方法显著提高了翻译质量，无论是通过自动检查还是人工阅读输出，且没有产生巨大的额外成本。
强化自我训练（ReST）用于语言建模
出处: Reinforced Self-Training (ReST) for Language Modeling
发布: 2026年2月19日
📄 English Summary
Reinforced Self-Training (ReST) for Language Modeling
Reinforced Self-Training (ReST) is a novel approach that enables language models to self-improve by learning from their own generated examples, rather than relying on human guidance at every step. This method involves the model creating a large number of sentences and later learning from this collection, allowing the same examples to be reused multiple times. Since training occurs with stored examples instead of real-time feedback, it relies on offline data, saving time and computational resources while enabling teams to reuse previous work. In translation tests, the method produced noticeably better translations, as confirmed by both automated checks and human evaluations, all without incurring significant additional costs.