Jietang argues that scaling laws need more than parameter counts

In this post, Jietang argues that parameter count alone no longer captures the scaling choices behind capable models. He contrasts training-compute results, inference costs, mixture-of-experts tradeoffs, and effective depth before presenting GLM-5.3 as a controlled test of post-training as a remaining scaling lever.