compressive-evaluation

The paper introduces LatentGRM, a latent evaluation framework that performs rubric-guided reward modeling in continuous latent space via semantics-preserving chunk compression, achieving competitive preference accuracy against SFT judges while compressing evaluation trajectories 8.9–9.2× and reducing inference time 6.1–7.0× at 4B/8B scales.