When Pairwise Preferences Cycle: A Reliability-Anchored Preference Walk for Video GRPO
Pairwise preference feedback has become a widely adopted supervision signal for reinforcement learning in video generation models, as pairwise comparisons provide a more reliable alternative than pointwise scalar ratings for evaluating the quality of generated videos. Specifically, we define a preference graph for each GRPO group under a pairwise evaluator, where nodes represent sampled rollouts and edges encode pairwise preferences. Auditing 6,976 on-policy comparison graphs, we find that cyclic preferences are prevalent and persist throughout training. Naive pairwise reward aggregation based on average win rates discards the underlying comparison structures and treats each rollout indep...
| Authors | Xinrui Lin, Fan Li, Shumin Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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