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

May 20, 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
COLM 2026 Efficient Reasoning Workshop

Abstract

Accepted at COLM 2026 Efficient Reasoning Workshop.

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
Final-year 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.