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An adaptive switching algorithm for knowledge distillation that dynamically balances off-policy teacher guidance and on-policy student exploration to mitigate exposure bias in small language models.
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This project is a research implementation tied to a specific arXiv paper. While it addresses a valid technical gap in knowledge distillation (the trade-off between teacher guidance and student exploration), it currently lacks community adoption (0 stars) and the methodology is likely to be subsumed by the proprietary, more advanced distillation pipelines used by frontier labs to create 'mini' and 'flash' model variants.
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