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English(EN) When to Follow the Teacher: Advantage-Based On-Policy Distillation

新的蒸馏方法AOPD提高了学生模型的性能

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为基于优势的在线策略蒸馏（AOPD）的新方法，以提高机器学习中在线策略蒸馏的有效性。与假设教师指导总是​​有益的标准方法不同，AOPD根据教师动作对当前学生模型的反事实优势进行加权。这种方法可以加强有益的教师动作，同时弱化那些无助于学生成功的动作。AOPD可以使用标准的学生轮出和终端奖励进行有效估算，无需额外的轮出或奖励评估。在数学、编码和MedQA数据集上的实验表明，AOPD在外部教师和自蒸馏场景中均优于现有的蒸馏和强化学习基线。 AI

影响 这项新的蒸馏技术可以更有效地训练AI模型，尤其是在编码和医学问答等复杂领域。

排序理由 该集群包含一篇详细介绍新机器学习方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的蒸馏方法AOPD提高了学生模型的性能

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该集群包含一篇详细介绍新机器学习方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mahdi Sabbaghi, George Pappas, Hamed Hassani, Adel Javanmard ·

    When to Follow the Teacher: Advantage-Based On-Policy Distillation

    arXiv:2610.31563v1 Announce Type: new Abstract: Online Policy Distillation (OPD) often outperforms offline methods such as Supervised Fine-Tuning (SFT) by training on trajectories sampled directly from the student. However, standard OPD trains the student to follow the teacher at…

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