EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via
  Action Pruning
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.
6830109ea20ebb4738e76931
Jiawei Liu
claimed_verified
2025-05-26T08:15:09.111Z
6747d38098fe79433a8c4580
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/BrcsTfusqfu9p9uv1NeX6.png
Jiawei Liu
user
Jiawei1222
6830109ea20ebb4738e76932
Qisi Chen
6830109ea20ebb4738e76933
Jianshu Zhang
admin_assigned
2025-05-27T12:42:26.545Z
65d8b0f0661492b25c6623de
https://cdn-avatars.huggingface.co/v1/production/uploads/65d8b0f0661492b25c6623de/c6LPDse8NIV_3BHIu8dYe.png
Jianshu Zhang
user
Sterzhang
6830109ea20ebb4738e76934
Quan Liu
6830109ea20ebb4738e76935
Defu Lian
admin_assigned
2025-05-27T12:42:48.468Z
66ed026076a8038cb4ae6053
/avatars/99b6527da6b66c6b5df3fc8261587322.svg
Defu Lian
user
dove-ustc
EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via
  Action Pruning
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.
EquivPruner reduces token consumption and improves reasoning accuracy by pruning semantically equivalent actions in LLM searches, leveraging a new dataset for mathematical equivalence.
Large Language Models (LLMs) semantic similarity semantically equivalent actions EquivPruner MathEquiv equivalence detector GSM8K