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Extrapolation isn't about memorizing tasks, it's about learning the relationships *between* them, and this relational approach unlocks surprisingly strong generalization.
Training LLMs to explicitly optimize for how they're *actually* used at inference time unlocks substantial performance gains compared to standard fine-tuning.