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Fine-tuning LLMs to emulate specific cultural values improves average accuracy, but simultaneously widens the disparity in performance between demographic subgroups.
LLMs can achieve state-of-the-art reasoning accuracy with significantly fewer tokens by rewarding intermediate reasoning steps that maximize information gain and maintain monotonic progress.
