RLVR: Are long rollout windows really necessary?
In brief
- CCR enables processing multiple problems simultaneously with a shared budget.
- 82% reduction in tokens used for training on 4B models.
- Answer quality is preserved despite shorter rollout windows.
This study examines a method to reduce the costs of long rollout windows in reinforcement learning with verifiable rewards (RLVR). It introduces Concurrent Constraint Reinforcement (CCR), which processes multiple problems simultaneously with a shared budget while maintaining answer quality. Results show significant token savings without performance loss.
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