title: VeriThinker: Learning to Verify Makes Reasoning Model Efficient
summary: Large Reasoning Models (LRMs) excel at complex tasks using Chain-of-Thought
  (CoT) reasoning. However, their tendency to overthinking leads to unnecessarily
  lengthy reasoning chains, dramatically increasing inference costs. To mitigate
  this issue, we introduce VeriThinker, a novel approach for CoT compression.
  Unlike conventional methods that fine-tune LRMs directly on the original
  reasoning task using synthetic concise CoT data, we innovatively fine-tune the
  model solely through an auxiliary verification task. By training LRMs to
  accurately verify the correctness of CoT solutions, the LRMs inherently become
  more discerning about the necessity of subsequent self-reflection steps,
  thereby effectively suppressing overthinking. Extensive experiments validate
  that VeriThinker substantially reduces reasoning chain lengths while
  maintaining or even slightly improving accuracy. When applied to
  DeepSeek-R1-Distill-Qwen-7B, our approach reduces reasoning tokens on MATH500
  from 3790 to 2125 while improving accuracy by 0.8% (94.0% to 94.8%), and on
  AIME25, tokens decrease from 14321 to 10287 with a 2.1% accuracy gain (38.7% to
  40.8%). Additionally, our experiments demonstrate that VeriThinker can also be
  zero-shot generalized to speculative reasoning. Code is available at
  https://github.com/czg1225/VeriThinker
paper_authors._id: 6833cc35015eb19058ed83d9
paper_authors.name: Zigeng Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:10:12.285Z
paper_authors._id: 65811eeaa2284a018e51f1ba
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/dH8UZj6Kk5HJkI1DItCNm.jpeg
paper_authors.fullname: Zigeng Chen
paper_authors.type: user
paper_authors.user: Zigeng
paper_authors._id: 6833cc35015eb19058ed83da
paper_authors.name: Xinyin Ma
paper_authors._id: 6833cc35015eb19058ed83db
paper_authors.name: Gongfan Fang
paper_authors._id: 6833cc35015eb19058ed83dc
paper_authors.name: Ruonan Yu
paper_authors._id: 6833cc35015eb19058ed83dd
paper_authors.name: Xinchao Wang
paper_title: VeriThinker: Learning to Verify Makes Reasoning Model Efficient
paper_summary: Large Reasoning Models (LRMs) excel at complex tasks using Chain-of-Thought
  (CoT) reasoning. However, their tendency to overthinking leads to unnecessarily
  lengthy reasoning chains, dramatically increasing inference costs. To mitigate
  this issue, we introduce VeriThinker, a novel approach for CoT compression.
  Unlike conventional methods that fine-tune LRMs directly on the original
  reasoning task using synthetic concise CoT data, we innovatively fine-tune the
  model solely through an auxiliary verification task. By training LRMs to
  accurately verify the correctness of CoT solutions, the LRMs inherently become
  more discerning about the necessity of subsequent self-reflection steps,
  thereby effectively suppressing overthinking. Extensive experiments validate
  that VeriThinker substantially reduces reasoning chain lengths while
  maintaining or even slightly improving accuracy. When applied to
  DeepSeek-R1-Distill-Qwen-7B, our approach reduces reasoning tokens on MATH500
  from 3790 to 2125 while improving accuracy by 0.8% (94.0% to 94.8%), and on
  AIME25, tokens decrease from 14321 to 10287 with a 2.1% accuracy gain (38.7% to
  40.8%). Additionally, our experiments demonstrate that VeriThinker can also be
  zero-shot generalized to speculative reasoning. Code is available at
  https://github.com/czg1225/VeriThinker
paper_ai_summary: VeriThinker reduces the length of complex reasoning chains in Large Reasoning Models (LRMs) by fine-tuning them on a verification task, thereby decreasing inference costs without significantly sacrificing accuracy.
paper_ai_keywords: Large Reasoning Models (LRMs) Chain-of-Thought (CoT) reasoning CoT compression verification task reasoning chain lengths reasoning tokens accuracy DeepSeek-R1-Distill-Qwen-7B MATH500 AIME25 speculative reasoning