On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters
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.
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.
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On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters
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the idea of persisting tiny adapters on top of trillion-parameter foundations as personal state really resonates with memory-augmented lm work, reframing peft as a durable, population-scale substrate rather than a budget tweak. the δ-mem and rl-native init variants are clever, but i keep wondering how an explicit cross-adapter memory layer (shared keys/values) would interact with the personal memories and governance they propose. a tighter bridge to retrieval or external memory literature could help with long-term consistency and stability in multi-user settings, especially during long rl loops. the arxivlens breakdown helped me parse the method details; there is a solid walkthrough here that covers section 3 well https://arxivlens.com/PaperView/Details/on-the-scaling-of-peft-towards-million-personal-models-of-trillion-parameters-3614-1a6ad351
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