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English(EN) TeachBack: Learning to Teach via Bidirectional Preference Optimization for On-Policy Self-Distillation

TeachBack框架通过双向偏好优化改进AI模型训练

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的基于策略的自蒸馏（OPSD）框架TeachBack，以增强语言模型的训练。TeachBack通过不仅从教师模型学习，还教会教师如何提供更有效的指导，来解决现有OPSD方法的局限性。这是通过控制提供给教师的参考解决方案的长度和风格来实现的，鼓励教师探索和重新解决问题，而不是仅仅依赖参考。在推理任务上的实验表明，与当前的OPSD技术相比，TeachBack显著提高了学生模型的性能。 AI

影响 通过提高自蒸馏技术的有效性来增强语言模型的训练。

排序理由 该集群包含一篇详细介绍AI模型训练新方法的论文。[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 →

TeachBack框架通过双向偏好优化改进AI模型训练

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该集群包含一篇详细介绍AI模型训练新方法的论文。[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) · Linfei Ji (Huazhong University of Science and Technology), Jiayun Li (Tsinghua University), Haozhao Wang (Huazhong University of Science and Technology), Siyu Jiang (Huazhong University of Science and Technology), Zehao Wang (Harbin Institute of Technolo… ·

    TeachBack：通过双向偏好优化进行同策略自蒸馏教学学习

    arXiv:2610.29167v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) enables a language model to learn from a Teacher instantiated from the same model but given access to privileged information, such as a reference solution. We find that access to sufficient privi…

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