Language models don't learn in-context all at once: control of the answer flips first, and accuracy catches up trillions of tokens later
Language models do not acquire in-context learning in one step. A controlled diagnostic separates copying the nearest demonstration from composing mappings spread across the prompt; across 17 models about a third prefer the copied answer and the rest the rule-consistent one, and in OLMo-2-7B dependence on the requested value overtakes the conflicting example hundreds of billions of tokens before accuracy rises above chance. Training on synthetic definition-and-query documents flips the preference, while exposure to definitions or suppressing copy-favoring heads does not.