Through the PRISM: Preference Representation in Intermediate States of Video Diffusion Models

Video reward models are central to aligning video diffusion models with human preferences, yet existing ones are external vision-language models that score clean, decoded pixels. Scoring an intermediate denoising state therefore requires either finishing the remaining denoising steps or decoding a noisy latent that the reward model cannot reliably interpret. We ask whether a video generator can itself act as the evaluator, discriminating human preferences directly from its noisy latents. We propose PRISM (Preference Representation in Intermediate States of Diffusion Models), a latent video reward model that keeps a pre-trained video diffusion transformer frozen, reads features from its early blocks, and maps them to a timestep-conditioned reward through a lightweight query-based aggregation head with only 21.8M trainable parameters. PRISM outperforms pixel-space reward models at high noise levels on two benchmarks while remaining competitive with 8B-parameter vision-language reward models on clean videos. This noise robustness enables early-stage Best-of-N sampling: selecting among candidates after only a few denoising steps reduces sampling cost by up to 10x and surpasses the strongest pixel-space baseline on each generator at 3-4x lower cost.