RewardVerse: Rubric-Guided Policy Optimization for Video Reward Modeling
RewardVerse uses dynamic rubrics and RGPO to stabilize video reward scores and reduce scalar drift.
RewardVerse is a rubric-based video reward framework that first generates explicit evaluation criteria, then scores videos against that rubric to limit scalar drift. Rubric-Guided Policy Optimization warms up the scorer with self-evolving seed rubrics and then jointly trains the rubric generator and scorer against human ratings. The authors report state-of-the-art pointwise and pairwise results on the 16-dimensional EvalVerse benchmark and external datasets. They position the signal as a reward for reinforcement learning in video generation.
- Dynamic rubrics sit between the query and the scorer
- RGPO warms up the scorer, then jointly trains the rubric generator
- Claims SOTA pointwise and pairwise scores on EvalVerse
- Aimed at stable rewards for video-generation reinforcement learning
From the original article
1 min read at huggingface.co
Reinforcement learning (RL) is vital for optimizing video generation models, with a robust reward model (RM) serving as the cornerstone. However, existing video reward models often produce unstable scalar scores because they directly map complex, subjective video quality into a single score without explicit evaluation criteria.
By Zhenchen Tang, Yang Li, Songlin Yang · huggingface.co
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.