A curriculum-learning fine-tuning method that uses self-distilled data as an easy first stage and an adaptive scheduler to shift to real data improves LLM-based recommendation accuracy.
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Bridging the Gap: Self-Optimized Fine-Tuning for LLM-based Recommender Systems
A curriculum-learning fine-tuning method that uses self-distilled data as an easy first stage and an adaptive scheduler to shift to real data improves LLM-based recommendation accuracy.