On-policy distillation (OPD) trains a student model on trajectories sampled from its own policy, providing dense token-level supervision by minimizing the divergence between the teacher's and student's output distributions at each token. Although existing OPD approaches effectively distill strong reasoning capabilities into student models, they inherently inherit uncurated chain-of-thought traces, exacerbating overthinking and reasoning redundancy. To address this limitation, we introduce COPD, a contrastive OPD framework. Specifically, for each token generated by the student, a frozen teacher evaluates the current student state under two contrasting prompts that induce low and high reasoning effort, respectively. The resulting difference in log-probabilities serves as a token-level advantage signal to guide the policy update. Rather than merely mimicking a single teacher distribution, COPD guides the student model toward acquiring concise and efficient reasoning strategies. We evaluate COPD across 9 multimodal benchmarks covering both reasoning and understanding tasks. Empirical results show that COPD substantially reduces reasoning length without hurting task performance, consistently improving efficiency across different tasks and model scales. In addition, this contrastive paradigm extends naturally to on-policy self-distillation (OPSD), establishing self-contrastive supervisory signals that enable a single model to compress its own reasoning without an external teacher.

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