{
  "id": 17176448,
  "title": "Turing LLM Post-Training: Calibrated Distillation and Efficient Dynamic-MoE Execution",
  "url": "https://urgent.news/2026/10/29/turing-llm-post-training-calibrated-distillation-and-efficient",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-29T11:52:20.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.39876v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "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 with teacher-wise loss normalization and clipping, with an execution path that…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}