AI RESEARCH
On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters
arXiv CS.LG
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ArXi:2606.02437v1 Announce Type: new Parameter-efficient fine-tuning (PEFT) is usually treated as a cheaper alternative to full fine-tuning. We study a broader role: small trainable adapters as persistent local state on top of strong shared foundation models. In this framing, the base model provides shared competence while adapters carry instance-specific behavior such as preferences, skills, tool habits, and memory-like updates.