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Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning

Paper · 2026 · YongKang Liu, Xingle Xu, Ercong Nie et al.

Summary

Parameter-efficient fine-tuning (PEFT) methods such as LoRA often match full fine-tuning (FFT) on standard benchmarks. It is less clear whether they hold up on harder tasks like reasoning and instruction-following, or under adversarial inputs. The authors address this with a theoretical analysis backed by experiments.

They model PEFT as a mapping from a small parameter space (k dimensions) into the full weight space (d dimensions). From this they prove four things:

  • PEFT's reachable solutions form a strict, measure-zero subset of FFT's.
  • An upper bound limits how far PEFT can shift the model's outputs.
  • Adding more trainable parameters yields diminishing gains.
  • PEFT benefits less than FFT from additional training data and is more sensitive to perturbations.

They test these claims on 20 datasets (classification, generation, reasoning, instruction-following) and 11 adversarial test sets, including AdvGLUE and Adversarial SQuAD, using models such as RoBERTa-base/large and LLaMA2-7B.

PEFT works well on routine tasks, but FFT keeps an edge on complex and adversarial settings. The authors therefore recommend FFT when resources permit and call for new approaches beyond current PEFT designs.

Key points

  • Data efficiency: In few-shot runs, LoRA beat FFT with fewer than 20 training samples and the two were roughly equal at 20. Above 20, FFT pulled ahead. Going from 5 to 200 samples improved FFT by 9.05% and LoRA by 7.08%.
  • Marginal benefit of data: The theory puts the ratio of PEFT's to FFT's marginal benefit from extra data at roughly O(k/d), which is much less than 1.
  • More LoRA parameters: Raising the LoRA rank (8, 16, 32) barely changed accuracy or robustness. By contrast, unfreezing more top layers (1–24) clearly helped FFT on AdvGLUE and Adversarial SQuAD.
  • Memory cost: An efficient FFT method, HiFT, used slightly less GPU memory than the PEFT methods tested. On LLaMA2-7B it needed 40.11 GB versus 40.69–43.24 GB for PEFT.
  • Code: Released on GitHub as misonsky/PEFTEval.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Shanghai Jiao Tong UniversityShanghai · China149 works · 16,161 citations🎓Northeastern UniversityShenyang · China42 works · 1,940 citations🎓Shandong UniversityJinan · China17 works · 789 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

Cite

Cite as YongKang Liu, Xingle Xu, Ercong Nie, Zijing Wang, Shi Feng, Daling Wang, Qian Li and Hinrich Schuetze. 2026. Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.2208

@inproceedings{liu2026look,
  title = {{Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning}},
  author = {YongKang Liu, Xingle Xu, Ercong Nie, Zijing Wang, Shi Feng, Daling Wang, Qian Li and Hinrich Schuetze},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.2208},
  url = {https://doi.org/10.18653/v1/2026.acl-long.2208}
}

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