Mira Murati@miramuratiAI 评分4242作为我们对开放协作科学承诺的一部分，我们正通过资助计划扩大研究人员和讲师对 Tinker 的免费访问。
Today we’re announcing research and teaching grants for Tinker: credits for scholars and students to fine-tune and experiment with open-weight LLMs. Read more and apply at: https://thinkingmachines.ai/blog/tinker-research-and-teaching-grants/
Mira Murati@miramuratiAI 评分3636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。
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/