Meta-learning view of ICL is the perspective that language models perform implicit learning algorithms at inference time using prompt examples as training data - it treats forward-pass adaptation as learned optimization behavior acquired during pretraining.

What Is Meta-learning view of ICL?

• Definition: Model is interpreted as implementing task adaptation rules encoded in parameters.
• Inference Learning: Prompt demonstrations act like mini-training episodes processed at runtime.
• Behavior Signature: ICL improves as demonstrations become more representative and structured.
• Relation: Complementary to Bayesian views, with focus on learned update dynamics.

Why Meta-learning view of ICL Matters

• Capability Explanation: Helps explain why larger models show stronger few-shot adaptation.
• Prompt Strategy: Suggests examples should expose task function clearly and consistently.
• Architecture Insight: Motivates analysis of circuits that implement in-forward adaptation.
• Benchmarking: Frames ICL tasks as tests of learned meta-optimization ability.
• Safety: Adaptive behavior can generalize both helpful and harmful patterns quickly.

How It Is Used in Practice

• Episode Design: Construct prompts as clean support-set and query-set structures.
• Scaling Analysis: Compare meta-learning signatures across model sizes and checkpoints.
• Circuit Mapping: Use patching to identify components that mediate runtime adaptation.

Meta-learning view of ICL is a dynamic-learning interpretation of prompt-based model adaptation - meta-learning view of ICL is most useful when linked to measurable adaptation dynamics and causal mechanisms.