Search papers, labs, and topics across Lattice.
The paper introduces PRIME, a new benchmark designed to evaluate verifiers for process-outcome alignment in mathematical and engineering problem-solving, addressing the limitations of outcome-centric verification methods in Reinforcement Learning with Verifiable Rewards (RLVR). PRIME consists of 2,530 high-difficulty STEM problems and is used to demonstrate that existing verifiers often fail to identify flaws in the derivation process. The authors show that RLVR training using verifiers selected based on PRIME significantly improves performance on challenging math problem sets, and that PRIME's accuracy strongly correlates with RLVR training effectiveness.
Current verifiers often reward correct answers derived from flawed reasoning, but PRIME offers a benchmark to identify and select verifiers that actually penalize incorrect derivations.
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.
Tsinghua AI