VeriThinker: Learning to Verify Makes Reasoning Model Efficient
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
6833cc35015eb19058ed83d9
Zigeng Chen
claimed_verified
2025-05-26T08:10:12.285Z
65811eeaa2284a018e51f1ba
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/dH8UZj6Kk5HJkI1DItCNm.jpeg
Zigeng Chen
user
Zigeng
6833cc35015eb19058ed83da
Xinyin Ma
6833cc35015eb19058ed83db
Gongfan Fang
6833cc35015eb19058ed83dc
Ruonan Yu
6833cc35015eb19058ed83dd
Xinchao Wang
VeriThinker: Learning to Verify Makes Reasoning Model Efficient
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
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.
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