做RLHF或RLVR的团队终于有了更聪明的奖励设计——POW3R解决了静态评分标准浪费训练信号的问题，做多模态或文本模型对齐的开发者可以直接参考实验设置。
强化学习中的可验证奖励（RLVR）在自动检查正确性时很有效，但许多模型行为需要同时满足多个定性标准。基于评分标准的奖励（rubric-based rewards）通过聚合多个标准来解决这一问题，但静态聚合会混淆人类赋予的重要性和当前优化信号的有效性。研究人员提出POW3R框架，它能在训练过程中动态调整各标准的奖励权重，优先关注当前能区分模型输出的标准。实验表明，POW3R在30个基线策略/指标比较中赢了24个，平均奖励和严格完成率均优于传统方法，且训练速度提升2.5-4倍。
Not Every Rubric Teaches Equally: Policy-Aware Rubric Rewards for RLVR
Reinforcement learning with verifiable rewards has made post-training highly effective when correctness can be checked automatically. However, many important model behaviors require satisfying several qualitative criteria at once. Rubric-based rewards address this setting by grading prompt-specific criteria and aggregating them into a scalar reward. Yet standard static aggregations conflate a criterion's human-assigned importance with its current usefulness as an optimization signal. We show that this assumption breaks down in rubric RL: many important criteria are already saturated or currently unreachable, while criteria that distinguish rollouts are not necessarily those with the largest human weights. We introduce POW3R, a policy-aware rubric reward framework that preserves human weights and category balance as the rubric objective while adapting criterion-level reward weights during training. POW3R uses rollout-level contrast to emphasize criteria that currently separate the policy's outputs, making the GRPO reward more informative without changing the underlying evaluation target. Across three base policies on two datasets spanning multimodal and text-only settings, POW3R wins $24$ of $30$ base-policy/metric comparisons, improving both mean rubric reward and strict completion (the fraction of prompts whose response satisfies every required rubric criterion) over vanilla GRPO with rubric rewards, and reaches the same plateau in $2.5$--$4\times$ fewer training steps. Rubric rewards should therefore distinguish what should matter in the final answer from what can teach the current policy.