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Qwen Tongyi Lab proposes RLCSD to address the style drift problem in on-policy self-distillation, where the learning signal focuses on style tokens rather than task-critical reasoning tokens. Their method uses contrastive supervision to focus on task-relevant tokens, achieving consistent improvements over prior methods on reasoning benchmarks.