Mira Murati· @miramurati · X·· 2025-10-28AI 评分36
AI 导读
结合RL和SFT的优势，采用同策略蒸馏，这是一种有前景的方法，可用于训练小模型以提升领域表现和持续学习能力。
正文 · 原文
Combining the benefits of RL and SFT with on-policy distillation, a promising approach for training small models for domain performance and continual learning.
Our latest post explores on-policy distillation, a training approach that unites the error-correcting relevance of RL with the reward density of SFT. When training it for math reasoning and as an internal chat assistant, we find that on-policy distillation can outperform other approaches for a fraction of the cost. https://thinkingmachines.ai/blog/on-policy-distillation/在 X 查看被引用的帖子
来源：Mira Murati · x.com