Beyond Rotations: AuroOFT for Expressive Quantized Orthogonal Fine-Tuning

arXiv:2608.05253 · cs.LG · Submitted 2026-08-05 · Read on arXiv

Yue Han, Dianlin Wang

cs.LG

Submitted: 2026-08-05

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• QLoRA — 25× in this paper · explained in 2 episodes
• Fine-Tuning — 12× in this paper · explained in 5 episodes
• Quantization — 11× in this paper · explained in 15 episodes
• Expressivity — 10× in this paper · explained in 2 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Accuracy — 6× in this paper · explained in 1 episode
• Parameter-Efficient Fine-Tuning — 3× in this paper · explained in 3 episodes
• Augmentation — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Parameter Efficiency — 2× in this paper · explained in 3 episodes
• Ablation Study — 1× in this paper · explained in 3 episodes
• Evaluation Protocol — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 7 episodes
• Parameterization — 1× in this paper · explained in 2 episodes
• Structural Properties — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 16 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Transformers — 1× in this paper · explained in 2 episodes

Sources

• Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
• PeriodicLoRA: Breaking the Low-Rank Bottleneck in LoRA Optimization

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