2 papers
cs.LG2026
Beyond Rotations: AuroOFT for Expressive Quantized Orthogonal Fine-Tuning
Yue Han, Dianlin Wang
Quantized orthogonal fine-tuning (qoft) enables parameter-efficient adaptation of low-bit language models by learning structured activation rotations before frozen quantized weight…
cs.LG2026
Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning
Yue Han, Ziniu Liu
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 generalization a…