RLCSD: New Reinforcement Learning Method Improves Reasoning Models Through Contrastive Self-Distillation
Researchers have proposed RLCSD, a reinforcement learning method that uses contrastive on-policy self-distillation to improve reasoning in large language models. The work identifies a problem called 'privilege-induced style drift,' where existing self-distillation approaches cause models to focus on stylistic rather than task-relevant tokens, leading to shorter and less stable outputs. RLCSD addresses this by contrasting learning signals from correct and incorrect hints, yielding more task-focused supervision and outperforming prior methods on mathematical and logical reasoning benchmarks.
A team of researchers has introduced RLCSD (Reinforcement Learning with Contrastive on-policy Self-Distillation), a new training method aimed at improving the reasoning capabilities of large language models. The paper identifies a previously underexplored failure mode in on-policy self-distillation (OPSD), where models trained with privileged context—such as a verified solution hint—tend to produce shorter, more stylistically direct outputs rather than learning task-relevant reasoning. The authors term this phenomenon 'privilege-induced style drift,' arguing that the learning signal in standard OPSD concentrates on surface-level style tokens rather than semantically meaningful, task-bearing ones. RLCSD mitigates this by computing a contrastive signal: the difference between teacher-student gaps under a correct hint versus a wrong hint, effectively canceling out style-related shifts common to both conditions. Experiments conducted on Qwen3 models (1.7B, 4B, and 8B parameters) and OLMo-3-7B-Think demonstrate consistent improvements over GRPO and prior OPSD baselines across mathematical and logical reasoning tasks. The authors also show that the contrastive principle is modular and can be integrated into existing OPSD frameworks, and that its insights generalize to cross-model distillation settings. The paper spans 20 pages with 9 figures and 9 tables, and was submitted to arXiv in June 2026.
What's missing
The study relies on benchmarks in mathematical and logical reasoning; generalization to other domains (e.g., coding, open-ended generation, or factual question answering) is not demonstrated. The paper has not yet undergone formal peer review, as it is a preprint.
What different sources said
- arXiv cs.LGCenter
RLCSD: Reinforcement Learning with Contrastive On-Policy Self-Distillation
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