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The capability jump in GLM-5.3 came entirely from scaled post-training and reinforcement learning on longer-horizon executable tasks, not from a larger base model or new pretraining run.
The most technically significant claim around Z.ai's GLM-5.3 launch is that the substantial improvement over GLM-5.2 was achieved purely through scaled post-training and RL on executable tasks, using the same 743B base model rather than a new pretraining run. ✦ AI generated
AINews (Z.ai) · Latent Space · 2026-08-14 · original ↗
The key claim many engineers highlighted is that the capability jump came entirely from scaled post-training/RL on longer-horizon executable tasks, not from a larger base model.
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- ·Gains came from post-training and RL only
- ·Same 743B base model as GLM-5.2
- ·No new pretraining run conducted
- ·Contrasts with scaling-up-base-model approach
- ·Scaled RL on executable tasks
- ·Longer-horizon task horizons enabled
- ·Post-training as primary capability lever
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