Curvilinear Demonstration Selection

Many-Shot CoT-ICL: Making In-Context Learning Truly Learn

arXiv:2605.13511v3 Announce Type: replace Abstract: While many-shot ICL achieves remarkable performance, prior studies of its scaling behavior have mainly focused on non-reasoning tasks. In this work, we study many-shot ICL on reasoning tasks, with a particular focus on many-shot chain-of-thought in-context learning (CoT-ICL). Analyzing across non-reasoning and reasoning tasks and across non-reasoning and reasoning-oriented LLMs, we identify several distinctive properties of many-shot CoT-ICL.