IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation
Instruction-following is a foundational capability of large language models (LLMs), with its improvement hinging on scalable and accurate feedback from judge models. However, the reliability of current judge models in instruction-following remains underexplored due to several deficiencies of existing meta-evaluation benchmarks, such as their insufficient data coverage and oversimplified pairwise evaluation paradigms that misalign with model optimization scenarios. To this end, we propose IF-RewardBench, a comprehensive meta-evaluation benchmark for instruction-following that covers diverse instruction and constraint types. For each instruction, we construct a preference graph containing all pairwise preferences among multiple responses based on instruction-following quality. This design enables a listwise evaluation paradigm that assesses the capabilities of judge models to rank multiple responses, which is essential in guiding model alignment. Extensive experiments on IF-RewardBench reveal significant deficiencies in current judge models and demonstrate that our benchmark achieves a stronger positive correlation with downstream task performance compared to existing benchmarks. Our codes and data are available at https://github.com/thu-coai/IF-RewardBench.
69ac11469893f58272f73e02
Bosi Wen
claimed_verified
2026-03-09T09:40:44.551Z
64bd41744561d0aca2982279
/avatars/64b5b0dc196d7246ba6a86f1b5314134.svg
Wenbosi
JaneGreen
user
JaneGreen
69ac11469893f58272f73e03
Yilin Niu
69ac11469893f58272f73e04
Cunxiang Wang
69ac11469893f58272f73e05
Xiaoying Ling
69ac11469893f58272f73e06
Ying Zhang
69ac11469893f58272f73e07
Pei Ke
69ac11469893f58272f73e08
Hongning Wang
69ac11469893f58272f73e09
Minlie Huang
IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation
Instruction-following is a foundational capability of large language models (LLMs), with its improvement hinging on scalable and accurate feedback from judge models. However, the reliability of current judge models in instruction-following remains underexplored due to several deficiencies of existing meta-evaluation benchmarks, such as their insufficient data coverage and oversimplified pairwise evaluation paradigms that misalign with model optimization scenarios. To this end, we propose IF-RewardBench, a comprehensive meta-evaluation benchmark for instruction-following that covers diverse instruction and constraint types. For each instruction, we construct a preference graph containing all pairwise preferences among multiple responses based on instruction-following quality. This design enables a listwise evaluation paradigm that assesses the capabilities of judge models to rank multiple responses, which is essential in guiding model alignment. Extensive experiments on IF-RewardBench reveal significant deficiencies in current judge models and demonstrate that our benchmark achieves a stronger positive correlation with downstream task performance compared to existing benchmarks. Our codes and data are available at https://github.com/thu-coai/IF-RewardBench.
A comprehensive meta-evaluation benchmark for instruction-following is introduced, featuring a preference graph approach that enables listwise evaluation of judge models' ability to rank multiple responses, revealing deficiencies in current judge models and demonstrating stronger correlation with downstream task performance.
instruction-following judge models meta-evaluation benchmark preference graph listwise evaluation model alignment downstream task performance