Author

Daijun Zhu

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#artificial intelligence Preprint Oct 2026

Turing LLM Post-Training: Calibrated Distillation and Efficient Dynamic-MoE Execution

We study post-training for dynamically routed mixture-of-experts (MoE) models, focusing on integrating independently optimized specialists while keeping consolidation efficient. Our approach combines calibrated multi-teacher on-policy distillation (C-MOPD), which matches each selected teacher on a local Top-$K$ support...

Yue He, Jia-Hui Hu, Sha-Man Tang et al. · 0 citations

#artificial intelligence Preprint Aug 2026

TuringLLM: Efficiently Scaling Foundation Models Toward Physical AI

Despite its compact active-parameter budget, Turing-20B-A2B achieves, at the base-model stage, overall general capability exceeding Qwen3-8B Base and approaching Qwen3.5-9B Base, while maintaining strong long-context performance and favorable prefill-latency scaling.

Yu-Heng Zhang, Yi-Zhao Wang, Daijun Zhu et al. · 0 citations