PRFL: reward feedback learning entirely in latent space, no VAE decoding needed
gowthami_s · x · 2026-10-17
arXiv paper 2511.21541 proposes PRFL (Process Reward Feedback Learning) for video generation alignment.
Core idea
- Existing video reward models take pixel-space inputs, so ReFL optimization only kicks in near the end of denoising after costly VAE decoding — heavy memory overhead, slow training, and no early-stage supervision for motion and structure.
- The authors show pretrained video generation models are naturally suited to reward modeling in noisy latent space, since they already process noisy latents at arbitrary timesteps and preserve temporal information.
Method
- They reuse part of the pretrained video-gen backbone to build a latent reward model that scores intermediate noisy latents, eliminating VAE decoding, cutting memory overhead, and enabling gradient backprop through the full denoising chain.
Paper: https://arxiv.org/abs/2511.21541
Related event: PRFL: Latent Process Reward Feedback Speeds Up Video Generation Alignment(5 posts)→
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