VeriThinker: Learning to Verify Makes Reasoning Model Efficient
Authors: name: Zigeng Chen, name: Xinyin Ma, name: Gongfan Fang, name: Ruonan Yu, name: Xinchao Wang

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