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. We further interpret these findings by viewing many-shot CoT-ICL as in-context test-time learning rat
The paper investigates the scaling behavior of in-context learning, specifically Many-Shot CoT-ICL, for reasoning tasks, aligning with current efforts to enhance LLM capabilities and efficiency.
Improving few-shot learning for reasoning tasks without extensive fine-tuning is crucial for developing more capable and adaptable AI models, reducing computational costs and increasing accessibility.
The identified distinctive properties of Many-Shot CoT-ICL suggest new avenues for optimizing in-context learning strategies, potentially accelerating the development of more robust AI agents.
- · AI developers
- · LLM researchers
- · AI-powered platforms
- · Cloud computing providers
- · Companies relying on outdated fine-tuning methods
- · Models with poor in-context learning capabilities
Further research into 'in-context test-time learning' will refine LLM training and deployment.
More capable and efficient AI models will accelerate automation in knowledge work and complex problem-solving.
The development of highly autonomous AI agents could fundamentally alter white-collar workflows and industry structures.
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Read at arXiv cs.CL