FUSE: Ensembling Verifiers with Zero Labeled Data
Verification of model outputs is rapidly emerging as a key primitive for both training and real-world deployment of large language models (LLMs). In practice, this often involves using imperfect LLM judges and reward models since ground truth acquisition can be time-consuming and expensive. We introduce Fully Unsupervised Score Ensembling (FUSE), a method for improving verification quality by ensembling verifiers without access to ground truth correctness labels. The key idea behind FUSE is to control conditional dependencies between verifiers in a manner that improves the unsupervised performance of a class of spectral algorithms from the ensembling literature. Despite requiring zero gro...
| Authors | Joonhyuk Lee, Virginia Ma, Sarah Zhao et al. |
|---|---|
| Primary category | stat.ML |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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