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This paper introduces Guidance Contrastive Policy Optimization (GCPO), a novel algorithm that enables per-token credit assignment in reinforcement learning by contrasting model predictions under positive and negative prompts, consistently outperforming GRPO and DAPO baselines on text-to-image generation and chain-of-thought reasoning benchmarks.