The Chinese open-weight release pattern: same architecture, big gains via post-training
bendee983 · x · 2026-10-27
bendee983 spots a common pattern in open-weight releases from Chinese AI labs: a model ships with impressive results, then weeks later a same-architecture update jumps significantly — e.g. Kimi-K3 → K3.1, GLM-5.2 → 5.3, DeepSeek-V4.1 Flash → 1017.
- In all cases the architecture stayed constant; gains came from continued post-training with better data or algorithms.
- He suspects GPU/resource constraints push labs to ship suboptimal versions before training fully finishes.
- Artificial Analysis charts show these updated models climbing both the Intelligence Index and cost-per-task Pareto line.
- Takeaway: models still have large untapped learning capacity unlocked by the right data, training regime, and algorithms.
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