Rufus-Air: An Open LLM Post-Training Recipe
Rufus-Air releases a reproducible eight-stage post-training recipe on GLM-4.5-Air-Base that beats the official post-trained release.
Rufus-Air documents an open post-training pipeline on GLM-4.5-Air-Base (106B-A12B) consisting of eight serial stages: SFT, Reasoning RL, Coding RL, Instruction-Following RL, General Agent, Coding Agent, Search Agent, and RLHF. It uses only open-source components and public data with no new human annotation or in-house distillation teacher. Key findings include that diverse high-quality SFT establishes a strong capability floor, difficulty filtering keeps RL prompts productive, and reward reliability guides stage ordering. The result improves over the official GLM-4.5-Air post-trained release and is competitive with similarly sized open models.
- Eight-stage recipe: SFT, three RL capability stages, three agent stages, then RLHF
- Built entirely on public data and open-source infrastructure, no new human annotation
- Outperforms official GLM-4.5-Air post-trained release; competitive at its size class
- Organizations
- Rufus-Air
- AI models
- GLM-4.5-Air
From the original article
1 min read at huggingface.co
Rufus-Air is an open and reproducible post-training recipe on GLM-4. 5-Air-Base (106B-A12B), organized as a serial pipeline of eight stages: SFT, Reasoning RL, Coding RL, Instruction-Following RL, General Agent, Coding Agent, Search Agent, and RLHF. We document the data, reward design, infrastructure, stage order, and stagewise results needed to reproduce the recipe.
By Chia-Yuan Chang, Renyuan Cheng, Rui Feng · huggingface.co
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