Learning to Solve, Forgetting to Retain: Correct-Set Turnover in RLVR

Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models, yet aggregate accuracy gains can conceal a hidden cost: previously solved problems may regress as training proceeds. We characterize this phenomenon as correct-set turnover, capturing the coupled dynamics of solution acquisition and regression, and make retention an explicit training objective alongside acquisition. In group-relative RLVR, mastered prompts provide no reward gradient when all rollouts are correct, yet can still drift under updates driven by other prompts. This observation motivates a timely-review principle: previously mastered prompts should be revisited before such regression accumulates. We propose ReMind, a retention-aware review mechanism that tracks mastered prompts, periodically revisits them with fresh rollouts, and requeues those that regress. Through pre-rollout batch replacement, ReMind preserves the per-step rollout budget. Across 20 image-text, video, and text-only reasoning benchmarks, ReMind improves average accuracy by 3.81 and 3.44 percentage points over GRPO and DAPO, respectively, while consistently improving retention across modalities and algorithms.