LLMs excel at few-shot in-context learning (ICL) - learning from a few input-output examples ("shots") provided in context at inference, without any weight updates.

The wider application of end-to-end learning methods to embodied decision-making domains remains bottlenecked by their reliance on a superabundance of training data representative of the target domain.

Furthermore, our S et-based N eural network E ncoder (SNE) takes into consideration the hierarchical computational structure of neural networks.

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Moreover, the model tends to generalize well to unseen tasks that did not appear in the training data.