Z.ai CEO Jie Tang on GLM 5.3: The era of parameter counting is over, post-training is the new scaling law

[AINews] Death of Params: Z.ai CEO Jie Tang on GLM 5.3 and the new Post-training Scaling Law

Jie Tang posted a long thread on X arguing that parameter count alone is meaningless—you need data volume, compute allocation, and deployment conditions. GLM-5.3's gains come entirely from RL on long-horizon environments, some simulating days of engineer work. They built synthetic pipelines that auto-generate executable, verifiable environments and reward signals, pushing the model to own complex tasks end-to-end. Tang identified 5 scaling knobs including MoE sparsity, and noted that finding software vulnerabilities requires holding 20+ inference-step causal chains, not memorization. The post does not disclose GLM-5.3's exact parameter count or release date.

Why it matters: Jie Tang personally explains GLM 5.3's post-training scaling law with concrete experimental cases (simulated cluster diagnosis and optimization), not just rhetoric. But the source is a paid newsletter excerpt, and key numbers (specific speedup ratios, task success rates) aren'...