Variance-aware Reward Modeling with Anchor Guidance
Standard Bradley--Terry (BT) reward models are limited when human preferences are pluralistic. Although soft preference labels preserve disagreement information, BT can only express it by shrinking reward margins. Gaussian reward models provide an alternative by jointly predicting a reward mean and a reward variance, but suffer from a fundamental non-identifiability from pairwise preferences alone. We propose Anchor-guided Variance-aware Reward Modeling, a framework that resolves this non-identifiability by augmenting preference data with two coarse response-level anchor labels. Building on this, we prove that two anchors are sufficient for identification, develop a joint training objecti...
| Authors | Shuxing Fang, Ruijian Han, Liangyu Zhang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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