给AI打分前先写评分标准，视频生成奖励模型不再乱给分
给视频生成模型打分，现在的AI奖励模型常常直接给一个分数，但不同提示词下分数会漂移——同一个视频，换个问法分数就崩了。这篇论文的做法很朴素：先让模型自己写出明确的评分标准（比如“动作连贯性”“光影一致性”），再按标准打分，而不是凭感觉给分。研究者还设计了两阶段训练：先用种子标准预热评分器，再联合优化标准生成器和评分器，让标准能随问题自适应。在16维基准和外部数据集上，它缓解了分数漂移，点对点和成对评估都达到最优。这不是你明天能用的工具，但它指向一个趋势：让AI的评判先有依据，再谈优化。
📄 原文摘要(英文)
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. This leads to scalar drift, where the scoring scale collapses or shifts across different prompts, making the reward unreliable for RL. Drawing inspiration from professional human annotation engineering, we address this problem with RewardVerse, a rubric-based video reward framework that introduces a dynamic rubric as an intermediate representation between the evaluation query and the scorer. Instead of unconstrained direct scoring, RewardVerse first generates explicit evaluation criteria and then performs rubric-guided scoring, providing a stable semantic anchor that mitigates scalar drift. To efficiently optimize this collaborative pipeline, we propose Rubric-Guided Policy Optimization (RGPO), a two-stage training algorithm. RGPO first warms up the scorer using self-evolving seed rubrics and then jointly optimizes the rubric generator to produce query-adaptive evaluation criteria while continuously aligning the scorer with human ratings. Extensive experiments on the 16-dimensional EvalVerse benchmark and external datasets demonstrate that RewardVerse mitigates scalar drift, achieves state-of-the-art performance on both pointwise and pairwise evaluation, and provides a robust and interpretable reward signal for RL in video generation.