Good Reasoning Makes Good Demonstrations: Implicit Reasoning Quality Supervision via In-Context Reinforcement Learning

Reinforcement Learning with Verifiable Rewards (RLVR) improves reasoning in large language models but treats all correct solutions equally, potentially reinforcing flawed traces that arrive at correct answers by chance. We observe that \emph{better reasoning makes better demonstrations}: high-quality solutions serve as more effective in-context examples than low-quality ones. We term this teaching ability $\mathbf{\text{Demonstration}}\mathbf{\text{Utility}}$, and show that the policy model’s own in-context learning ability provides an efficient way to measure it, yielding a quality signal termed $\mathbf{\text{Evidence}}\mathbf{\text{Gain}}$. To leverage this signal during training, we introduce $\mathbf{\text{In-Context}}\mathbf{\text{RLVR}}$, which prepends demonstrations before each rollout. Theoretically, we prove that this simple input modification implicitly reweights rewards by a factor approximately proportional to Evidence Gain, assigning higher weights to high-quality traces without requiring costly computation. Experiments on mathematical reasoning benchmarks demonstrate consistent improvements in both accuracy and reasoning quality over standard RLVR baselines. Our codes and datasets are available at https://github.com/Mithas-114/IC-DAPO.