AI RESEARCH
GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models
arXiv CS.CL
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ArXi:2605.01256v1 Announce Type: new A promising paradigm for adapting instruction-tuned language models is to learn task-specific updates on a pretrained base model and subsequently merge them into the instruction-tuned model. However, existing approaches typically treat the instruction-tuned model as a passive target that is only involved at the final merging stage, without guiding the