Adapter
A small set of trainable parameters inserted into a pretrained model. During fine-tuning, the backbone weights are frozen and only the adapter is updated. It belongs to parameter-efficient fine-tuning (PEFT) methods, with low training cost and little disruption to the original model's capabilities.
Timeline (last 90 days)
Rubric-CEPR distills the validated best candidate back into the editor through a lightweight adapter, updating only a small number of parameters rather than retraining the entire model.
A drawback of the Adapter method is increased inference latency, because information flow must pass serially through the additionally inserted modules.
All Facts (2)
Rubric-CEPR distills the validated best candidate back into the editor through a lightweight adapter, updating only a small number of parameters rather than retraining the entire model.
50%UnverifiedA drawback of the Adapter method is increased inference latency, because information flow must pass serially through the additionally inserted modules.
50%