唐杰说后训练是关键，GLM 5.3 实测却被自己的安全策略卡住了

TL;DR - A hands-on comparison found that GLM 5.3’s expanded post-training improved long-horizon coding, tool use, delivery speed, and product polish over GLM 5.2. However, its stricter safety behavior repeatedly blocked benign steps in an automated development workflow, highlighting an integration challenge for agentic coding systems.

• Both models delivered an offline browser-based 3D driving game from real OpenStreetMap data, including vehicle physics, traffic rules, navigation, persistence, documentation, and smoke tests.
• GLM 5.3 finished in 50 minutes 47 seconds versus GLM 5.2’s 66 minutes 1 second, while producing a more polished experience with improved lighting, road signs, HUD elements, and driving feedback.
• The models used the same base model; the article attributes GLM 5.3’s gains to larger-scale post-training on realistic, long-running engineering tasks involving tools, code modification, and validation.
• GLM 5.3’s enhanced security checks triggered refusals in Claude Code’s automated approval flow, stalling development despite the underlying task being benign and technically achievable.