From Harm to Help: Turning Reasoning In-Context Demos into Assets for Reasoning LMs

Haonan Wang, Weida Liang, Zihang Fu +8

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…