CompassJudger-2: Towards Generalist Judge Model via Verifiable Rewards
Authors: name: Taolin Zhang, name: Maosong Cao, name: Alexander Lam, name: Songyang Zhang, name: Kai Chen

Recently, the role of LLM-as-judge in evaluating large language models has
gained prominence. However, current judge models suffer from narrow
specialization and limited robustness, undermining their capacity for
comprehensive evaluations. In this work, we present CompassJudger-2, a novel
generalist judge model that overcomes these limitations via a task-driven,
multi-domain data curation strategy. Central to our approach is supervising
judgment tasks with verifiable rewards, guiding intrinsic critical reasoning
through rejection sampling to foster robust, generalizable judgment
capabilities. We introduce a refined learning objective with margin policy
gradient loss to enhance performance. Empirically, CompassJudger-2 achieves
superior results across multiple judge and reward benchmarks, and our 7B model
demonstrates competitive judgment accuracy with significantly larger models
like DeepSeek-V3 and Qwen3-235B-A22B. Additionally, we propose JudgerBenchV2, a
comprehensive benchmark evaluating cross-domain judgment accuracy and rank
consistency to standardize judge model evaluation. These contributions advance
robust, scalable LLM judgment and establish new performance and evaluation
standards.