NeurIPS 2025

LIMOPro: Reasoning Refinement for Efficient and
Effective Test-time Scaling
Yang Xiao1
Jiashuo Wang1
Ruifeng Yuan1
Chunpu Xu1
Kaishuai Xu1
Wenjie Li1†
Pengfei Liu2,3†
1The Hong Kong Polytechnic University
2Shanghai Jiao Tong University
3SII
yang-alan.xiao@connect.polyu.hk
csjwang@comp.polyu.edu.hk
Abstract
Large language models (LLMs) have demonstrated remarkable reasoning capa-
bilities through test-time scaling approaches, particularly when fine-tuned with
chain-of-thought (CoT) data distilled from more powerful large reasoning models
(LRMs). However, these reasoning chains often contain verbose elements that
mirror human problem-solving, categorized as progressive reasoning (the essential
solution development path) and functional elements (verification processes, alter-
native solution approaches, and error corrections). While progressive reasoning
is crucial, the functional elements significantly increase computational demands
during test-time inference. We introduce PIR (Perplexity-based Importance Re-
finement), a principled framework that quantitatively evaluates the importance of
each reasoning step based on its impact on answer prediction confidence. PIR sys-
tematically identifies and selectively prunes only low-importance functional steps
while preserving progressive reasoning components, creating optimized training
data that maintains the integrity of the core solution path while reducing verbosity.
Models fine-tuned on PIR-optimized data exhibit superior test-time scaling proper-
ties, generating more concise reasoning chains while achieving improved accuracy
(+0.9% to +6.6%) with significantly reduced token usage (-3% to -41%) across
challenging reasoning benchmarks (AIME, AMC, and GPQA Diamond). Our
approach demonstrates strong generalizability across different model sizes, data
sources, and token budgets, offering a practical solution for deploying reasoning-
capable LLMs in scenarios where efficient test-time scaling, response time, and
computationa...