This paper investigates In-Context Learning (ICL) models, particularly those employing transformers, from a learning-to-learn perspective. The authors theoretically demonstrate that ICL models are expressive enough to emulate existing meta-learning algorithms, such as gradient-based, metric-based, and amortization-based approaches. Their findings suggest that ICL learns data-dependent optimal algorithms during pre-training, which, while powerful, can limit generalizability to out-of-distribution or novel tasks. To address this, the study proposes applying techniques from classical deep networks, like meta-level meta-learning and curriculum learning, to enhance ICL's domain adaptability and accelerate convergence during the pre-training phase.

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