Unverified50% confidenceOpinionExact time
Anti-distillation clauses face enormous challenges in practical enforcement, as vendors find it very difficult to prove that a new model was trained on their outputs, especially after the training data has been mixed and cleaned.
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8/31/2026
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Unverified训练秘诀（Training Recipe）包括数据配比策略、学习率调度曲线、过滤规则、对齐技术等，往往比模型架构本身更难被复现76% similarUnverifiedAfter large models entered the post-pretraining era, the capability gains from simply stacking parameters have become increasingly diminishing, and vendors have turned to the post-training phase in search of breakthroughs.73% similarUnverifiedSimply stacking parameters without sufficient training data results in a model that is large but not capable; a leap in parameters must be accompanied by a proportionally expanded training corpus and computing power to realize capability gains.73% similarUnverifiedTraditional large model training relies heavily on manually annotated data and external corpus collection, which are typically the bottlenecks constraining iteration speed.72% similarVerifiedTraining data poisoning can implant persistent backdoors at the model weight level, posing far greater risks than application-layer jailbreaks, but the implementation threshold is higher and typically requires control over the training pipeline.72% similar
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