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Hyperfitting's surprising generation improvements aren't just temperature scaling – they stem from a "Terminal Expansion" in the final transformer block that dynamically reorders token ranks.
Forget temperature tuning: Min-$k$ sampling finds the "semantic cliff" in your LLM's logits, delivering robust and high-quality text even when other methods fall apart.
