LatentGRM Cuts Reward-Model Inference Cost 6-7x via Latent-Space Judging
Mingqing Yuan · hf · 2026-10-12
LatentGRM performs generative reward modeling in latent space: semantic chunking, compression, and reconstruction let the model make pairwise judgments over compact continuous trajectories without verbalizing evaluations, with a separate interpreter reconstructing text for offline inspection.
Results:
- At matched training data and backbones (4B and 8B), preference accuracy is competitive with explicit SFT judges
- LatentGRM-8B compresses evaluation trajectories 8.9–9.2x and cuts total judge inference time 6.1–7.0x at vote@5
- Controlled rubric interventions confirm criterion-dependent preference information survives compression
Takeaway: continuous latent evaluation substantially reduces inference cost while preserving judgment quality.
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