关于扩展定律的思考
模型扩展不等于只增加参数：数据、生命周期推理成本、MoE 激活参数与有效深度、任务类型以及后训练都会改变最优配置。作者以 Kaplan、Chinchilla、MoE 和 GLM-5.3 为线索，说明下一步最值得扩展的旋钮未必是模型规模。
Thoughts About Scaling Law
Model scaling is not just parameter growth. Data, lifetime inference cost, MoE activation and effective depth, task mix, and post-training all shift the optimum; Kaplan, Chinchilla, MoE research, and GLM-5.3 show that the next useful scaling dial may not be model size.
https://x.com/jietang/status/2089941544581403107
August 19, 2026 15
