A Stitch in Time Saves Nine: Proactive Self-Refinement for Language
  Models
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.
68a5320b6cf0bf898542ec35
Jinyi Han
claimed_verified
2025-08-20T08:51:44.028Z
67ed200efd37e21a47149075
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/Sg7Sxp1LAFbRY906EJoiU.png
Jinyi Han
user
JinyiHan
68a5320b6cf0bf898542ec36
Xinyi Wang
68a5320b6cf0bf898542ec37
Haiquan Zhao
68a5320b6cf0bf898542ec38
Tingyun li
68a5320b6cf0bf898542ec39
Zishang Jiang
68a5320b6cf0bf898542ec3a
Sihang Jiang
68a5320b6cf0bf898542ec3b
Jiaqing Liang
68a5320b6cf0bf898542ec3c
Xin Lin
68a5320b6cf0bf898542ec3d
Weikang Zhou
68a5320b6cf0bf898542ec3e
Zeye Sun
68a5320b6cf0bf898542ec3f
Fei Yu
68a5320b6cf0bf898542ec40
Yanghua Xiao
A Stitch in Time Saves Nine: Proactive Self-Refinement for Language
  Models
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.
ProActive Self-Refinement (PASR) dynamically enhances LLM outputs during generation, reducing token consumption and improving accuracy.
self-refinement large language models (LLMs) iterative refinement reactive process internal state evolving context ProActive Self-Refinement (PASR) Qwen3-8B