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 a...
| Authors | Bosi Wen, Yilin Niu, Cunxiang Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 March 2026 |
| Last updated at source | 16 April 2026 |
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