Unverified50% confidenceFactExact time
The distillation process of LoopOPD occurs on rollouts generated by the student itself rather than on external data, providing dense supervision signals without the need to introduce external teacher models or privileged information.
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10/9/2026
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UnverifiedOutput distillation involves directly using text generated by a teacher model—such as dialogues and reasoning chains—as training data to fine-tune a student model, without requiring access to the teacher's internal parameters.68% similarVerifiedIn offline distillation, training data is generated once by the teacher model and fixed, with the student model learning from a static dataset.66% similarUnverified模型蒸馏是指用大型教师模型的输出训练更小的学生模型，仅需调用API获取输出，不涉及权重复制，其法律边界模糊，全球尚无先例性司法判决明确定性62% similarUnverifiedDistillation can be viewed as an implicit, data-constrained form of model merging—distilling a student using the averaged outputs of multiple teacher models is effectively performing a soft merge at the level of output distributions.62% similarUnverifiedPure generative models mix all training data before producing output, making it impossible for users to trace the evidence chain behind any single conclusion—a limitation compared to the RAG retrieval mechanism.62% similar
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