Revisiting Reinforcement Learning with Verifiable Rewards from a Contrastive Perspective

In three linesConSPO, a new sequence-level policy optimization approach, improves GRPO by replacing clipped ratio-based scores with length-normalized log-probabilities and using an InfoNCE-style contrastive objective. Evaluated on mathematical reasoning benchmarks, ConSPO outperforms several RLVR baselines.

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