RLCSD: New Reinforcement Learning Method Improves Reasoning Models Through Contrastive Self-Distillation
Researchers propose RLCSD, a reinforcement learning technique that addresses a problem called "privilege-induced style drift" in on-policy self-distillation for reasoning models. The method uses contrastive learning to distinguish between correct and incorrect hints, focusing the learning signal on task-relevant tokens rather than stylistic ones. The approach shows consistent improvements over existing methods on mathematical and logical reasoning tasks across multiple model sizes.
A new arXiv paper introduces RLCSD (Reinforcement Learning with Contrastive on-policy Self-Distillation), a technique designed to improve how reasoning models learn from their own outputs. The authors identify a problem in existing on-policy self-distillation approaches: when models receive hints about correct solutions, they tend to learn stylistic patterns (like producing shorter outputs) rather than focusing on task-bearing tokens that actually solve problems. RLCSD addresses this by contrasting the model's behavior under correct hints against its behavior under incorrect hints, thereby suppressing style drift while preserving task-relevant learning signals. Experiments on Qwen3 models (1.7B, 4B, and 8B parameters) and Olmo-3-7B-Think demonstrate consistent improvements over GRPO and prior on-policy self-distillation methods on mathematical and logical reasoning benchmarks. The authors also show that the contrastive principle is generalizable and can enhance other existing on-policy distillation methods.
What's missing
The paper does not specify which mathematical and logical reasoning benchmarks were used for evaluation, nor does it provide quantitative performance comparisons (e.g., accuracy improvements or percentage gains). Additionally, computational cost and training efficiency comparisons with baseline methods are not detailed in the abstract.
What different sources said
- arXiv cs.LGCenter
RLCSD: Reinforcement Learning with Contrastive On-Policy Self-Distillation
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