Paper Detail
Chia-Yuan Chang, Renyuan Cheng, Rui Feng, Xiaotian Han, Yuan He, Hongye Jin, Linwei Li, Shiyang Li, Fenglin Liu, Xin Liu, Priyanka Nigam, Haoyang Wen, Zhenghao Xu, Zhuocheng Xu, Bing Yin, Qingyu Yin, Chao Zhang, Rongzhi Zhang, Zhihan Zhang, Zixuan Zhang, Tuo Zhao
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. Stages progress from basic to advanced capabilities and from hard, verifiable rewards to softer judge-based signals. Training builds on open-source components and public data, much of it used as released, without new human annotation or an in-house distillation teacher. Our main findings are that (i) diverse, high-quality SFT establishes a strong capability floor; (ii) difficulty filtering keeps RL prompts within a productive learning range; (iii) reward reliability provides a practical principle for ordering stages; and (iv) infrastructure and engineering choices are part of the recipe, not just an implementation detail. Rufus-Air improves over the official GLM-4.5-Air post-trained release and is competitive with similarly sized open models.
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@misc{chang2026rufus,
title = {Rufus-Air: An Open LLM Post-Training Recipe},
author = {Chia-Yuan Chang and Renyuan Cheng and Rui Feng and Xiaotian Han and Yuan He and Hongye Jin and Linwei Li and Shiyang Li and Fenglin Liu and Xin Liu and Priyanka Nigam and Haoyang Wen and Zhenghao Xu and Zhuocheng Xu and Bing Yin and Qingyu Yin and Chao Zhang and Rongzhi Zhang and Zhihan Zhang and Zixuan Zhang and Tuo Zhao},
year = {2026},
abstract = {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. Stages progress from basic to advanced capabilities and from hard, verifiable rewards to softer judge-based signals. Training build},
url = {https://huggingface.co/papers/2609.29421},
keywords = {huggingface daily},
eprint = {2609.29421},
archiveprefix = {arXiv},
}
{}