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English(EN) Unpacking the Compute Efficiency of Strong-to-Weak OPD against GRPO

On-Policy Distillation 相较于 GRPO 显示出计算效率的提升

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文探讨了与Group Relative Policy Optimization (GRPO) 相比，On-Policy Distillation (OPD) 在训练大型语言模型时的计算效率。虽然在使用外部教师时，GRPO在每训练FLOPs的准确率方面通常优于OPD，但OPD在自行训练检查点时表现出显著的效率提升，需要更少的FLOPs即可达到可比的准确率。研究还强调，多教师OPD可以将专门的GRPO模型整合到一个学生模型中，以显著降低总训练计算量的方式实现高性能。 AI

影响 这项研究可能带来更具计算效率的大型语言模型训练方法，从而降低人工智能开发的成本和环境影响。

排序理由 详细介绍LLM训练新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

On-Policy Distillation 相较于 GRPO 显示出计算效率的提升

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详细介绍LLM训练新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Zeyu Leo Liu, Yang Li, Bo Pang, Emre Can Acikgoz, Xuan-Phi Nguyen, Anurag Koul, Srijan Bansal, Erik Nijkamp, Shafiq Joty, Semih Yavuz ·

    深入解析强到弱OPD与GRPO的计算效率

    arXiv:2611.08453v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student on its own rollouts under a frozen teacher's per-token supervision. Does this dense signal reduce the training FLOPs needed to reach a target accuracy compared with reinforcement learn…

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