media Latent Space · 1h ago · agents

Z.ai proposes post-training scaling law and releases GLM 5.3; Ornith-1.5 launches with self-improvement

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Z.ai CEO Jie Tang argues that parameter count is insufficient for evaluating models, proposing a new scaling law based on data, compute allocation, and inference conditions. This framework underpins GLM 5.3, which achieves significant performance gains through reinforcement learning on long-horizon environments synthesized via recursive self-improvement pipelines.

Ornith-1.5 launches as an open-weight family (9B dense, 35B MoE, 397B MoE) featuring end-to-end self-improvement capabilities and strong agentic benchmarks.
UnslothAI releases Qwen3.8-27B GGUFs using Dynamic V3, claiming 10% higher accuracy at the same size and 1-bit quantization retaining 77% of BF16 accuracy on 8GB RAM.
GLM 5.3 ranks #2 on Terminal Bench and #3 on Legal Bench among open-weight models, while Claude Opus 5 leads quality in Agent Arena.

These developments highlight a shift toward evaluating model utility through complex, executable tasks rather than static parameter metrics.

Importance 2/3 New harness with differentiators Latent Space Zhipu AI DeepSeek AI agents Training methods

Benchmarks

Benchmark	Model	Score
SWE-bench Verified	Ornith-1.5	86%
DeepSWE	Ornith-1.5	56%
Humanity's Last Exam	Ornith-1.5	44.6%

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