MatryoshkaLoRA inserts a crafted diagonal matrix P into LoRA to learn accurate nested low-rank adapters that support dynamic rank selection with minimal performance drop.
IGU-LoRA : Adaptive rank allocation via integrated gradients and uncertainty-aware scoring
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Gradient-based proportional LoRA rank allocation under GRPO reduces accuracy by 4.5 points versus uniform allocation because GRPO gradients are flatter across layers and non-uniform ranks amplify importance differences.
citing papers explorer
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MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning
MatryoshkaLoRA inserts a crafted diagonal matrix P into LoRA to learn accurate nested low-rank adapters that support dynamic rank selection with minimal performance drop.
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Gradient-Based LoRA Rank Allocation Under GRPO: An Empirical Study
Gradient-based proportional LoRA rank allocation under GRPO reduces accuracy by 4.5 points versus uniform allocation because GRPO gradients are flatter across layers and non-uniform ranks amplify importance differences.