Unverified50% confidenceFactExact time
The tokenizer is usually fixed before model training and cannot be changed at the serving layer.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe weights of a large model are fixed once training is complete; each inference does not modify the weights, nor does it retain state for users on the server side.71% similarUnverified协议层的统一无法解决模型权重层面的训练偏好差异，即便采用同一套 schema 格式，模型对字段组合的内在偏好仍会因强化训练而分歧71% similarUnverifiedThe skill package pattern guides model behavior through a layer of engineering specification constraints without requiring the model to be retrained.70% similarUnverifiedPretrained classifiers are typically designed for fixed category systems, and when facing entirely new domains or highly specialized tasks may require fine-tuning or even retraining, and cannot replace large models in complex semantic understanding and generation.70% similarUnverified指令微调不需要从头训练，通常仅需基础训练计算量的1%以下即可显著改变模型行为模式69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1067576API
curl https://kongchang.com/api/v1/knowledge/claims/1067576MCP
get_claim(id=1067576)