AuroSFT for adapter-state multi-task fine-tuning replaces full-model rollback with adapter-state scheduling for multi-task fine-tuning
Read the original at arxiv.org→arXiv:2608.05250v1 Announce Type: new Abstract: Multi-task supervised fine-tuning (SFT) often casts a heterogeneous data mixture as a single optimization problem, even though different tasks may reach their best...
Original headline: "Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning"
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- Aug 7, 04:00 UTC arXiv cs.LG lead source Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning