Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes
  Correct Reasoning in Base LLMs
Authors: name: Xumeng Wen, name: Zihan Liu, name: Shun Zheng, name: Zhijian Xu, name: Shengyu Ye, name: Zhirong Wu, name: Xiao Liang, name: Yang Wang, name: Junjie Li, name: Ziming Miao, name: Jiang Bian, name: Mao Yang

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a
promising paradigm for advancing the reasoning capabilities of Large Language
Models (LLMs). However, a critical paradox clouds its efficacy: RLVR-tuned
models often underperform their base models on the Pass@K metric for
solution-finding, leading to the hypothesis that RLVR merely re-weights
existing reasoning paths at the cost of reasoning diversity. In this work, we
resolve this contradiction by identifying the source of the problem: the
Pass@K metric itself is a flawed measure of reasoning, as it credits correct
final answers that probably arise from inaccurate or incomplete chains of
thought (CoTs). To address this, we introduce a more precise evaluation metric,
CoT-Pass@K, which mandates that both the reasoning path and the final
answer be correct. We provide a new theoretical foundation that formalizes how
RLVR, unlike traditional RL, is uniquely structured to incentivize logical
integrity. Our empirical results are supportive: using CoT-Pass@K, we
observe that RLVR can incentivize the generalization of correct reasoning for
all values of K. Furthermore, by analyzing the training dynamics, we find
that this enhanced reasoning capability emerges early in the training process
and smoothly generalizes. Our work provides a clear perspective on the role of
RLVR, offers a more reliable method for its evaluation, and confirms its
potential to genuinely advance machine reasoning.