arXiv:2510.00192v3 Announce Type: replace-cross Abstract: Low-rank adaptation (LoRA) has become a widely used paradigm for parameter-efficient fine-tuning of large language models, yet its representational capacity often lags behind full fine-tuning. Within the context of LoRA, a key open question is how to obtain expressive low-rank…
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Source: cs.AI updates on arXiv.org
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