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Research · Aug 21, 2026

Z.ai chief executive Tang Jie: what really blocks post-training is no longer the model, it is the environment — and he says environments and graders can both be synthesised.

Model watch · This week (event date 08-20)

From the Aug 21, 2026 daily brief

Start with who this is. Z.ai (Zhipu) is a Chinese frontier model team, developer of the open-weight GLM series, and its chief executive Tang Jie was a professor in Tsinghua University's computer science department before this. He made two points about the new GLM-5.3 (Latent Space, 08-20). First, parameter count no longer describes a model — it has to be read alongside how much data went in, where the compute went, and who runs the thing under what conditions. The second point is the one that matters. A model reads vast amounts of text in pre-training, then goes through post-training with methods such as reinforcement learning, and most of the capability jump of the past two years came from the second stage. Scaling post-training is no longer hard because of the model. It is hard because of the environment: a simulated workplace the model can actually operate in, where success takes many steps to establish (hand it a whole machine-learning stack, ask it to diagnose the bottleneck in a training run, implement an optimisation, run the experiments, and deliver a measurable speed-up without breaking correctness). Environments like that used to be built by hand, one at a time. Tang says Z.ai has a production line that synthesises environments end to end, and for some tasks synthesises the reward signal too. More striking still: the grader is synthesised without sight of the reference answer, and it has to clear three checks — feed it the correct answer and it must pass, feed it "did nothing" and it must fail, feed it a half-finished state and it must fail as well.

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