A central question in alignment research concerns how language models acquire, represent, and arbitrate between competing values. The Helpful, Harmless, Honest (HHH) framework proposed by Askell et al. [33] formalizes alignment as the joint optimization of multiple normative objectives through supervised fine-tuning and reinforcement learning from human feedback. Building on this paradigm, Bai et al. [34] demonstrates that models can be trained to navigate tensions between helpfulness and harmlessness, and that larger models exhibit improved robustness in resolving such trade-offs under distributional shift.
How to handle async, part deux
，更多细节参见搜狗输入法
“我们如今的大脑结构与六万年前的智人相比，并未发生根本性改变。类比于大模型，这意味着基础参数与硬件架构未变，或许预训练数据有所增加，毕竟大家都接受了教育。但人类文明这些年的进步既然不是依靠脑容量扩大，那又是依靠什么实现的？”
pt (lines) 15.485 (lines: 5107)
In essence, pull-based reactivity is basically just a stack of function calls. I call a function, and it calls more functions if it needs to, then it returns a result. I can nest these functions recursively as much as I need, and the dependencies will all automatically be calculated for me.