
Z.ai Reframes Scaling Beyond Parameter Counts
Z.ai argues that model scaling should account for data, compute allocation, inference cost, sparsity, effective depth, and post-training—not parameters alone. The post presents GLM-5.3 as a controlled experiment using the same total and activated parameters as GLM-5.2 while scaling long-horizon environments and reinforcement learning for one month.





