Open post-training recipe for 106B GLM model published with full reproducibility
Researchers have published a fully reproducible post-training pipeline for GLM-4.5-Air-Base, a 106B parameter model, detailing an eight-stage progression from supervised fine-tuning through specialized reinforcement learning phases to final human feedback alignment. The work demonstrates that high-quality diverse SFT data establishes a strong foundation, while difficulty-calibrated RL prompts maintain training efficiency. By relying on open-source components and public datasets without proprietary distillation, the recipe lowers barriers for teams seeking to replicate frontier-model training workflows and provides concrete evidence on stage ordering and reward design tradeoffs that matter for scaling post-training infrastructure.
Modelwire context
ExplainerThe paper's real contribution isn't the model itself but the finding that stage sequencing and reward calibration have measurable efficiency tradeoffs. Most prior work either keeps recipes proprietary or omits ablations on ordering; this work quantifies what happens when you swap stages or change difficulty thresholds.
This connects directly to the calibration work published today. CORDIAL showed that LLMs systematically miscalibrate probability outputs on ordinal tasks, requiring post-hoc correction. Rufus-Air's emphasis on difficulty-calibrated RL prompts suggests the field is converging on a broader insight: raw model outputs need explicit calibration at multiple stages, not just at inference. The same pattern appears in Jev's alignment detection work from today, which also treats calibrated confidence as a first-class design goal rather than an afterthought.
If Alibaba or other teams publish ablations showing that swapping the RL stage order degrades final performance by more than 2-3 points on standard benchmarks, that validates the recipe's specificity. If instead other groups reproduce similar results with different orderings, the stage sequence matters less than the paper claims, and the real value is just having any open pipeline.
Coverage we drew on
- CORDIAL: Calibrating Ordinal LLM Outputs from Few Labels · arXiv cs.CL
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MentionsGLM-4.5-Air-Base · Rufus-Air · Alibaba
Modelwire Editorial
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