Unverified50% confidenceFactExact time
Both pplx-embed-v2-late models were trained from an 18B teacher model via representation distillation.
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Long-term
Relevance
10/22/2026
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Unverified同一天发布两个前沿级模型，透露出训练管线可复用性——同一套预训练基础模型可以通过不同后训练与压缩策略派生出多个版本。71% similarUnverifiedThe arXiv paper "RM-OPD: On-Policy Distillation for Generative Reward Models" proposes a two-stage process: first train multiple domain-specific reward model teachers, then integrate them into one generative student model through routed on-policy distillation, and this unified student model is said to surpass all of its distilled expert teachers.71% similarUnverifiedLLaVA的训练分为两个阶段：第一阶段是预训练对齐（冻结视觉编码器和语言模型，仅训练投影层），第二阶段是指令微调（解冻语言模型进行端到端训练）68% similarUnverifiedAcceleration approaches such as Turbo and Lightning introduce consistency distillation or adversarial distillation techniques, using a teacher model to supervise a student model in approximating the final output within very few steps.68% similar
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