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Openai/6939b23e-87cc-8001-bdd9-9546d1143ce9
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=== The repeat penalty discourages the King from picking the same warrior repeatedly. === * If a warrior has been chosen recently, the King loses a bit of confidence in that warrior. * This lowers the chance the warrior is picked again immediately. ===== - Imagine the King gets bored of picking the same hero over and over. ===== * If he already sent Warrior A on a mission recently, he’s slightly less likely to pick A again, even if A is strong. * This encourages variety in the King’s choices. ===== | | | ===== | --- | --- | | | | | | | | | | | | | | | | If you want, I can rewrite the full King/warrior story including Min-P and Repeat Penalty, keeping numbers and examples just like we did with top-k, top-p, and temperature. It makes it super easy to visualize LLM sampling behavior in one story. Do you want me to do that?
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