Unverified50% confidenceOpinionExact time
Small models have a hard upper limit on how much world knowledge they can accommodate; parameter scale determines how many factual pieces of information a model can memorize, and quantization cannot circumvent this physical constraint.
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9/21/2026
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VerifiedKnowledge distillation technology enables small models to extract a considerable proportion of capability from large models, undermining the validity of fixed compute thresholds as a proxy metric for capability.78% similarUnverifiedTotal parameter count reflects the upper limit of a model's knowledge capacity and expressive power, while activated parameter count determines the computational cost and latency during inference.74% similarUnverifiedThe knowledge of general-purpose large models comes from massive public corpora, which is broad but not deep.74% similarUnverified模型微调能将知识永久写入模型权重，但需要大量标注数据、算力成本高、迭代周期长72% similarUnverifiedThere is a positive correlation between a model's calibration capability and model scale; larger models can more finely distinguish between states of knowledge.72% similar
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