Adaptivity never hinders uniform approximation of task families but its advantages vary across four scenarios when moving from unrestricted to ReLU-realizable regimes.
L.How many pretraining tasks are needed for in-context learning of linear regression?arXiv preprint arXiv:2310.08391(2023)
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Transformer model with response-time auxiliary input adapts reward models to unseen human preference domains via in-context learning from demonstrations.
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Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning
Adaptivity never hinders uniform approximation of task families but its advantages vary across four scenarios when moving from unrestricted to ReLU-realizable regimes.
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In-Context Reward Adaptation for Robust Preference Modeling
Transformer model with response-time auxiliary input adapts reward models to unseen human preference domains via in-context learning from demonstrations.