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Alignment tuning shrinks LLM output diversity by 2-5x, new research shows
A new arXiv paper introduces the Branching Factor (BF), a metric quantifying output diversity in large language models, and finds that alignment tuning reduces this diversity by 2-5x overall—and up to 10x at early generation positions. The research suggests alignment doesn't fundamentally change model behavior but instead steers outputs toward lower-entropy token sequences already present in base models.