Hugging Face Daily Papers · June 2, 2026 · 3 min read

On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters

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Papers

arxiv:2606.02437

On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters

Published on Jun 1

· Submitted by

Andrew Chen on Jun 2

#2 Paper of the day

Mind Lab

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Abstract

Parameter-efficient fine-tuning can function as a compact substrate for persistent personal models by enabling small trainable adapters to store instance-specific behaviors on top of strong foundation models.

AI-generated summary

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. We organize the problem around three scaling axes: Scale Up, where stronger shared priors make small local updates more useful; Scale Down, where we study how small adapters can be while remaining reliable; and Scale Out, where many persistent adapted instances coexist. MinT provides one infrastructure example for managing adapter identity, revision, provenance, evaluation, and serving residency. Together, the results suggest that PEFT can be a compact substrate for persistent personal models rather than only a budget substitute for full fine-tuning.