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Adaptive Mutual Distillation for Balanced Multi-Task Post-Training of Large Language Models
Multi-task post-training of large language models (LLMs) aims to improve performance across tasks with unequal amounts of training data. Existing methods focus primarily on balancing task contributions during single-model training. Different task-balancing strategies can produce models with compleme
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