Reproduce “Post-Optimization Adaptive Rank Allocation for LoRA” on OpenResearch
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About this paper
Vishnuprasadh Kumaravelu, Sunil Gupta, P. K. Srijith
PARA trains one high-rank LoRA adapter, then compresses it after training. It applies one threshold to singular values across layers, retaining stronger update directions while assigning layers different ranks. With a SigLIP2 Base vision encoder, it averages 88.83% accuracy across seven image-classification datasets, versus 84.30% for rank-4 LoRA. Its compressed adapter has 75% fewer LoRA parameters than the rank-16 parent.
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