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Beyond Entropy: Correctness-Aware Advantage Shaping via Contrastive Policy Optimization

10 upvotes · 16 JUL 2026 · Weiwen Xu, Jia Liu, Hou Pong Chan et al.

This paper proposes a new method for advantage shaping in reinforcement learning called Contrastive Policy Optimization (CPO), which uses contrastive disagreement between reference-guided and vanilla generation distributions to indicate correctness. Practitioners might care because it can improve the effectiveness of reinforcement learning methods in generating correct responses.

reinforcement learningpolicy optimizationcontrastive learningcorrectness-aware methodsadvantage shaping