LIMOPro: Reasoning Refinement for Efficient and Effective Test-time
  Scaling
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.
6837210455e9bab4e9c302b1
Yang Xiao
claimed_verified
2025-05-28T19:13:52.090Z
6002c316698168af3bb9f4a6
https://cdn-avatars.huggingface.co/v1/production/uploads/6002c316698168af3bb9f4a6/M2J2QFCRc5RhYRoz7OuZN.png
yangxiao
user
YangXiao-nlp
6837210455e9bab4e9c302b2
Jiashuo Wang
6837210455e9bab4e9c302b3
Ruifeng Yuan
6837210455e9bab4e9c302b4
Chunpu Xu
6837210455e9bab4e9c302b5
Kaishuai Xu
6837210455e9bab4e9c302b6
Wenjie Li
6837210455e9bab4e9c302b7
Pengfei Liu
claimed_verified
2025-06-29T10:14:27.243Z
6144a0c4ff1146bbd84d9865
https://cdn-avatars.huggingface.co/v1/production/uploads/1661715958139-6144a0c4ff1146bbd84d9865.png
Pengfei Liu
user
Pengfei
LIMOPro: Reasoning Refinement for Efficient and Effective Test-time
  Scaling
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.
A framework called PIR refines the importance of reasoning steps in large language models by pruning low-importance functional elements, leading to more concise reasoning chains with improved accuracy and reduced computational demands.
large language models chain-of-thought large reasoning models progressive reasoning functional elements perplexity-based importance refinement token usage reasoning benchmarks AIME AMC GPQA Diamond