| title | TextReg: Mitigating Prompt Distributional Overfitting via Regularized Text-Space Optimization |
|---|---|
| authors | Lucheng Fu, Ye Yu, Yiyang Wang, Yiqiao Jin, Haibo Jin, B. Aditya Prakash, Haohan Wang |
| date | 2026-10-05 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.21318 |
| affiliation | Georgia Institute of Technology |
| upvotes | 6 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Large language models (LLMs) are highly sensitive to the prompts used to specify task objectives and behavioral constraints. Many recent prompt optimization methods iteratively rewrite prompts using LLM-generated feedback, but the resulting prompts often become longer, accumulate narrow sample-specific rules, and generalize poorly beyond the training distribution. We study this failure mode as prompt distributional overfitting and argue that it reflects a lack of representation control in discrete text-space optimization. We formalize this view through representational inefficiency, a dual-factor measure that decomposes prompt inefficiency into capacity cost and scope narrowness, attributing distributional prompt overfitting to their coupled growth during optimization. We propose TextReg, a regularization framework that realizes a soft-penalty objective through regularized textual gradients, combining Dual-Evidence Gradient Purification, Semantic Edit Regularization, and Regularization-Guided Prompt Update. Across multiple reasoning benchmarks, TextReg substantially improves out-of-distribution (OOD) generalization, with accuracy gains of up to +11.8% over TextGrad and +16.5% over REVOLVE. |
cite
@misc{fu2026textreg,
title={TextReg: Mitigating Prompt Distributional Overfitting via Regularized Text-Space Optimization},
author={Lucheng Fu and Ye Yu and Yiyang Wang and Yiqiao Jin and Haibo Jin and B. Aditya Prakash and Haohan Wang},
year={2026},
eprint={2605.21318},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.21318}
}