EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via
  Action Pruning
Authors: name: Jiawei Liu, name: Qisi Chen, name: Jianshu Zhang, name: Quan Liu, name: Defu Lian

Large Language Models (LLMs) excel at complex reasoning through search
algorithms, yet current strategies often suffer from massive token consumption
due to redundant exploration of semantically equivalent steps. Existing
semantic similarity methods struggle to accurately identify such equivalence in
domain-specific contexts like mathematical reasoning. To address this, we
propose EquivPruner, a simple yet effective approach that identifies and prunes
semantically equivalent actions during LLM reasoning search. We also introduce
MathEquiv, the first dataset we created for mathematical statement equivalence,
which enables the training of a lightweight equivalence detector. Extensive
experiments across various models and tasks demonstrate that EquivPruner
significantly reduces token consumption, improving searching efficiency and
often bolstering reasoning accuracy. For instance, when applied to
Qwen2.5-Math-7B-Instruct on GSM8K, EquivPruner reduced token consumption by
48.1\% while also improving accuracy. Our code is available at
https://github.com/Lolo1222/EquivPruner.