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DynaMiCS: Fine-tuning LLMs with Performance Constraints using Dynamic Mixtures
Multi-domain fine-tuning of large language models requires improving performance on target domains while preserving previous capabilities, such as general knowledge, instruction following, or safety evaluations. Existing data mixing strategies rely on fixed heuristics or adaptive rules that cannot e
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