Judging in Latent Space: Efficient Generative Reward Modeling via Semantics-Preserving Compression
Reward modeling often requires jointly representing and reasoning over multiple evaluation criteria, yet verbalizing this process token by token can incur substantial inference cost. Recent work on latent reasoning suggests that continuous states may support this computation more compactly. We introduce LatentGRM, a latent evaluation framework built on semantic chunking, compression, and reconstruction. By using the structure of rubric-guided evaluations to guide compression, LatentGRM learns compact continuous trajectories that support autonomous pairwise judgments without generating textual assessments. A separate interpreter reconstructs evaluation text from these trajectories, providi...
| Authors | Mingqing Yuan, Xiaobo Liang, Junwei Yang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 October 2026 |
| Last updated at source | 8 October 2026 |
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