SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD
Authors: name: Dongfang Li, name: Xiaodong Luo, name: Ruoyu Sun, name: Xuhui Chen, name: Linyuan Qiu, name: Jian Meng, name: Zhengxuan Lu, name: Yiting Wang, name: Yucheng Xie, name: Tao Guo, name: Tianxiang Fang, name: Jing Li, name: Sihang Chen, name: Shihao Hong, name: Chang Liu, name: Weihua Dai, name: Zirong Zeng, name: Ziwei Zhu, name: Zhuohan Wang, name: Zhengjun Yue, name: Igor Vasilyev, name: Min Liu, name: Weijian Sun, name: Xin Chen, name: Yingmeng Gao, name: Jinhua Zhou, name: Taolue Chen, name: Chenwei Wu, name: Dong Zhang, name: Wenlong Jin, name: Jinmin Xiang, name: Barkova Maria, name: Ushakov Anton, name: Xianfei Jin, name: Tian Ding, name: Zhihang Lin, name: Qian Chen, name: Linxin Yang, name: Mingzhe Yang, name: Bingwei Zhang, name: Hongzhang Yang, name: Fangxue Zhang, name: Shijun Qin, name: Jie Yu, name: Cuihua Hu, name: Tolstykh Vasiliy, name: Nosov Ivan, name: Abdullin Amir, name: Zhichen Zhou, name: Xin Zhang, name: Zhixiong Ning, name: Xutong Zhao, name: Junjie Huang, name: Jiajun Liu, name: Weiyan Kong, name: Zheng Zhang, name: Wenhan Luo, name: Lin Hu, name: Yangbo Guo, name: Li Zeng, name: Shihao Zeng, name: Baotian Hu, name: Min Zhang, name: Haizhou Li, name: Zhiquan Luo

Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed training, including severe memory pressure, non-overlapped communication overhead, and inefficient kernel execution. While most large-scale LLM training systems are built around GPU-based clusters, this report presents an end-to-end optimization practice on the Ascend NPU SuperPOD. Using the DeepSeek-V4 model family as the target workload, we develop a hierarchical optimization framework spanning model-level parallelism, computation-communication orchestration, and low-level kernel execution. The resulting system achieves 34.22% Model FLOPs Utilization (MFU) with a 2.93x improvement over the open-source baseline recipe while maintaining training stability. Building on this optimized infrastructure, we further establish a CPT and SFT workflow for complex Operations Research (OR) tasks. We refer to the integrated framework as SLAI T-Rex. Using DeepSeek-V4-Flash, we develop OR-oriented CPT and SFT data pipelines that combine collected domain resources with solver-verified synthetic optimization documents. The resulting dataset contains 10K high-quality SFT samples spanning four task categories and three problem representations. The specialized model achieves the highest average zero-shot Pass@1 score among the evaluated models, reaching 71.81% and outperforming GPT-5.4-Mini and the base DeepSeek-V4-Flash model by 3.98 and 11.27 percentage points, respectively. Overall, this work demonstrates a full-stack pathway from efficient trillion-parameter model post-training on Ascend infra to domain-specialized Flash models for solver-grounded mathematical modeling, advancing frontier-model systems for complex reasoning.