Parameter-Efficient Fine-Tuning
parameter-efficient-fine-tuning-4b4d4d50·3 events·first seen 26d agoAliases: Parameter-Efficient Fine-Tuning, parameter-efficient finetuning, PEFT (Parameter-Efficient Fine-Tuning)
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On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters
This paper reframes parameter-efficient fine-tuning (PEFT) not merely as a cheaper alternative to full fine-tuning, but as a substrate for persistent, instance-specific personal models layered atop shared foundation models. The authors analyze three scaling axes: Scale Up (stronger base models amplifying adapter utility), Scale Down (minimum viable adapter size), and Scale Out (managing millions of concurrent adapted instances). They introduce MinT as an infrastructure reference for adapter identity, versioning, provenance, evaluation, and serving at scale.
PEFT-Arena: Benchmarking Parameter-Efficient Finetuning via Stability-Plasticity Trade-offs
PEFT-Arena is a new benchmark that evaluates parameter-efficient finetuning methods jointly on downstream task performance and retention of pretrained general capabilities, framing the problem as a stability-plasticity dilemma. Across methods tested under comparable parameter budgets, orthogonal finetuning achieves the best Pareto frontier. The paper provides geometric analyses in both weight space (spectral/singular-value structure) and activation space (representation distortion metrics) to explain why different PEFT methods differ in forgetting behavior. A practical finding is that final SFT checkpoints often overshoot an optimal retention operating point, motivating path-wise rewinding as a post-hoc correction.
SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning
SMoA is a new parameter-efficient fine-tuning method that addresses LoRA's trade-off between rank size and parameter budget. It partitions model layers into spectral blocks and applies Hadamard-modulated low-rank branches to each diagonal block, enabling broader coverage of pretrained spectral directions without proportionally increasing trainable parameters. Theoretical analysis and empirical results on multiple tasks show SMoA outperforms LoRA and competitive LoRA-style baselines in lower-budget settings.