When Correct Solutions Repeat: Rarity-Aware Credit Redistribution for GRPO
Reinforcement learning with verifiable rewards (RLVR) com- monly optimizes each correct completion as an independent learning signal. In GRPO, this completion-level uniformity creates structure-level skew: recurring correct solution forms accumulate positive coefficient mass in proportion to how often they are sampled, while rare forms receive limited credit. We formalize this behavior as multiplicity-induced structure-level credit concentration and introduce a partition- conditioned rule that redistributes positive advantages accord- ing to cluster rarity. Cue-GRPO instantiates this rule with- out auxiliary-model inference by using deterministic Strategy Cues to construct rollout-local p...
| Authors | Zhe Cao, Miaowen Wen, Fangjiong Chen |
|---|---|
| Primary category | cs.AI |
| Published | 4 August 2026 |
| Last updated at source | 5 August 2026 |
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