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Public story · 2026-02-24 · source-backed

DSDR: Dual-Scale Diversity Regularization for LLM Training

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arXiv — Novel approach to maintaining output diversity during LLM fine-tuning. Practical implication for builders doing custom fine-tuning: prevents mode collapse where models converge to narrow response patterns. Uses dual-scale regularization — maintaining diversity at both token and sequence level. Early results show improved generalization without sacrificing task performance.


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DSDR: Dual-Scale Diversity Regularization for LLM Training | MindPattern