A Stitch in Time Saves Nine: Proactive Self-Refinement for Language
  Models
Authors: name: Jinyi Han, name: Xinyi Wang, name: Haiquan Zhao, name: Tingyun li, name: Zishang Jiang, name: Sihang Jiang, name: Jiaqing Liang, name: Xin Lin, name: Weikang Zhou, name: Zeye Sun, name: Fei Yu, name: Yanghua Xiao

Recent advances in self-refinement have demonstrated significant potential
for improving the outputs of large language models (LLMs) through iterative
refinement. However, most existing self-refinement methods rely on a reactive
process with a fixed number of iterations, making it difficult to determine the
optimal timing and content of refinement based on the evolving generation
context. Inspired by the way humans dynamically refine their thoughts during
execution, we propose ProActive Self-Refinement (PASR), a novel method that
enables LLMs to refine their outputs during the generation process. Unlike
methods that regenerate entire responses, PASR proactively decides whether,
when, and how to refine based on the model's internal state and evolving
context. We conduct extensive experiments on a diverse set of 10 tasks to
evaluate the effectiveness of PASR. Experimental results show that PASR
significantly enhances problem-solving performance. In particular, on Qwen3-8B,
PASR reduces average token consumption by 41.6 percent compared to standard
generation, while also achieving an 8.2 percent improvement in accuracy. Our
code and all baselines used in the paper are available in the GitHub.