# CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward Source: [CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:19:03.690Z Authors: Shudong Liu, Hongwei Liu, Junnan Liu, Linchen Xiao, Songyang Gao, Chengqi Lyu, Yuzhe Gu, Wenwei Zhang, Derek F. Wong, Songyang Zhang, Kai Chen Published: 2025-08-05T17:55:24.000Z ## Abstract Answer verification is crucial not only for evaluating large language models (LLMs) by matching their unstructured outputs against standard answers, but also serves as the reward model to guide LLM optimization. Most evaluation frameworks rely on regularized matching or employ general LLMs for answer verification, which demands extensive, repetitive customization for regex rules or evaluation prompts. Two fundamental limitations persist in current methodologies: 1) the absence of comprehensive benchmarks that systematically evaluate verification capabilities across different LLMs; and 2) the nascent stage of verifier development, where existing approaches lack both the robustness to handle complex edge cases and the generalizability across different domains. In this work, we develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward. It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types, including multi-subproblems, formulas, and sequence answers, while effectively identifying abnormal/invalid responses. We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of metaerror patterns to enhance CompassVerifier. We anticipate that CompassVerifier and VerifierBench will facilitate answer verification, evaluation protocols, and reinforcement learning research. Code and dataset are available at https://github.com/open-compass/CompassVerifier. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Verification on VerifyBench 1.0 (test)](): Verification; VerifyBench 1.0 (test); m@3 Accuracy: 96.3. - [Verification on VerifyBench Hard 1.0 (test)](): Verification; VerifyBench Hard 1.0 (test); Mean@3 Accuracy: 88.9. - [Verification on SCI-Bench](): Verification; SCI-Bench; Mean@3 Accuracy: 85.7. ## Original sources and implementation [Original paper]() [PDF]()