While model-based verifiers are essential for scaling Reinforcement Learning with Verifiable Rewards (RLVR), current outcome-centric verification paradigms primarily focus on the consistency between the final result and the ground truth, often neglecting potential errors in the derivation process. This leads to assigning positive rewards to correct answers produced from incorrect derivations. To bridge this gap, we introduce PRIME, a benchmark for evaluating verifiers on Process-Outcome Alignment verification in Mathematics and Engineering. Curated from a comprehensive collection of college-level STEM problems, PRIME comprises 2,530 high-difficulty samples through a consistency-based filtering pipeline. Through extensive evaluation, we find that current verifiers frequently fail to detect derivation flaws. Furthermore, we propose a process-aware RLVR training paradigm utilizing verifiers selected via PRIME. This approach substantially outperforms the outcome-only verification baseline, achieving absolute performance gains of 8.29%, 9.12%, and 7.31% on AIME24, AIME25, and Beyond-AIME, respectively, for the Qwen3-14B-Base model. Finally, we demonstrate a strong linear correlation ($R^2 > 0.92$) between verifier accuracy on PRIME and RLVR training effectiveness, validating PRIME as a reliable predictor for verifier selection.
ReverseMath: Answer Inversion for Scalable and Verifiable Mathematical Problem Generation
Center for Information and Language Processing（信息与语言处理中心）；LMU Munich（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心 (MCML)）；Saarland University（萨尔兰州大学）
Learning from Failures: Correction-Oriented Policy Optimization with Verifiable Rewards
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所信息处理实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Xiaohongshu Inc（小红书公司）
From Informal to Formal -- Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal Proofs
The Hong Kong University of Science and Technology（香港科学与技术大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Guangzhou Institute of Technology, Xidian University（西安电子科技大学广州研究院）；Chongqing University（重庆大学）；ICTT and ISN Laboratory, Xidian University（西安电子科技大学ICTT和ISN实验室）
