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This paper proposes drift-constrained optimization for fine-tuning instruct models, focusing on the direction of updates to improve target-task performance while preserving existing capabilities. Experiments show that layer-selective probing can enhance scientific reasoning and multilingual translation across multiple languages.
The paper applies repetition priming to show that base LLMs use automatic processing while instruct models exhibit controlled processing, with humans displaying a hybrid profile.