LIMOPro: Reasoning Refinement for Efficient and Effective Test-time
  Scaling
Authors: name: Yang Xiao, name: Jiashuo Wang, name: Ruifeng Yuan, name: Chunpu Xu, name: Kaishuai Xu, name: Wenjie Li, name: Pengfei Liu

Large language models (LLMs) have demonstrated remarkable reasoning
capabilities 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, alternative 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 Refinement), a principled framework that
quantitatively evaluates the importance of each reasoning step based on its
impact on answer prediction confidence. PIR systematically 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
properties, 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 computational efficiency are valuable constraints.