From Harm to Help: Turning Reasoning In-Context Demos into Assets for
  Reasoning LMs
Authors: name: Haonan Wang, name: Weida Liang, name: Zihang Fu, name: Nie Zheng, name: Yifan Zhang, name: Yao Tong, name: Tongyao Zhu, name: Hao Jiang, name: Chuang Li, name: Jiaying Wu, name: Kenji Kawaguchi

Recent reasoning LLMs (RLMs), especially those trained with verifier-based
reinforcement learning, often perform worse with few-shot CoT than with direct
answering. We revisit this paradox using high-quality reasoning traces from
DeepSeek-R1 as demonstrations and find that adding more exemplars consistently
degrades accuracy, even when demonstrations are optimal. A detailed analysis
reveals two mechanisms behind this decline: (i) semantic misguidance, where
high textual similarity leads the model to treat the target as the same as the
exemplar and to copy intermediate steps verbatim; and (ii) strategy transfer
failure, where the model struggles to extract useful reasoning strategies and
apply them to target questions. Guided by these, we introduce Insight-to-Solve
(I2S), a sequential test-time procedure that turns demonstrations into
explicit, reusable insights and derives a target-specific reasoning trace;
optionally, the reasoning is self-refined for coherence and correctness (I2S+).
Extensive experiments on diverse benchmarks show that I2S and I2S+ consistently
outperform both direct answering and test-time scaling baselines across open-
and closed-source models. Even for GPT models, our method helps: on AIME'25,
GPT-4.1 rises by +14.0%, and o1-mini improves by +2.7% on AIME and +1.7% on
GPQA, indicating that in-context demonstrations can be harnessed effectively
via insight-refine-solve framework.