研究人员推出了一种新的方法——轨迹精炼蒸馏（TRD），以改进大型语言模型的训练后过程。TRD解决了在线蒸馏中的“前缀失败”问题，该问题会导致密集型逐令牌监督产生碎片化梯度。通过在蒸馏前对轨迹级别的学生模型回放进行校正，TRD缓解了这一问题并增强了探索。该方法在各种基准测试和模型规模上都显示出了一致的性能提升。 AI
影响 通过改进蒸馏技术，增强了LLM的推理能力和准确性。
排序理由 该集群包含一篇详细介绍改进LLM训练新方法的学术论文。
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arXiv:2606.08432v1 Announce Type: new Abstract: On-policy distillation (OPD) has become a central post-training tool for large language models (LLMs), providing dense per-token teacher supervision along the student's own rollouts. In this work, we identify a common structural cau…
On-policy distillation (OPD) has become a central post-training tool for large language models (LLMs), providing dense per-token teacher supervision along the student's own rollouts. In this work, we identify a common structural cause underlying OPD, which we call prefix failure.…
On-policy distillation suffers from prefix failure where dense token-level supervision creates fragmented gradients; trajectory-refined distillation addresses this by correcting student rollouts at the trajectory level before distillation.