TextReg: Mitigating Prompt Distributional Overfitting via Regularized Text-Space Optimization

May 30, 2026·
Lucheng Fu
,
Ye Yu
,
Yiyang Wang
Yiqiao Jin
Yiqiao Jin
,
Haibo Jin
,
B. Aditya Prakash
,
Haohan Wang
· 1 min read
Abstract
Prompt optimization methods often overfit to narrow training distributions, producing prompts that fail to transfer. TextReg introduces a regularized text-space optimization objective that explicitly mitigates prompt distributional overfitting, improving robustness across tasks, models, and evaluation distributions.
Type
Publication
Under Review at ICLR 2027

Abstract

Prompt optimization methods often overfit to narrow training distributions, producing prompts that fail to transfer. TextReg introduces a regularized text-space optimization objective that explicitly mitigates prompt distributional overfitting.

Yiqiao Jin
Authors
Ph.D. Candidate in Computer Science
My research sits at the intersection of multimodal foundation models, intelligent agents, and spatial/physical intelligence. I build general-purpose models that perceive, reason, learn, and act in interactive virtual and physical environments.