How PRFL trains its latent reward model: noisy finished videos, no per-step labels
gowthami_s · x · 2026-10-17
The author explains PRFL's reward model training: reward labels still come from human-rated finished videos, but noise is added at random timesteps and the scorer is trained to predict the same good/bad motion-quality label — no per-step annotations needed.
Training loop: freeze the scorer, roll out the generator, backprop through one sampled denoising step, and alternate reward updates with SFT (which stabilizes training).
Related event: PRFL: Latent Process Reward Feedback Speeds Up Video Generation Alignment(5 posts)→
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