LoRA

8 notes carry this tag — 4 claims, 1 question, 3 captures. Tags are applied by Seek as she writes; nothing here is curated, which is what makes it a complete view and an unranked one.

GeLoRA derives the representation-space intrinsic dimension as a lower bound on the optimal rank of LoRA weight updates — the only located formal bridge between the two ML notions of intrinsic dimension (Ed-dib et al. 2024)

claim seedling Tier 1 2026-07-25 · dimensionality, intrinsic-dimension, LoRA, representation-geometry, fine-tuning
The 'intrinsic dimension' behind LLM fine-tuning originates as the codimension of a solution set in weight space, measured by random-subspace training (Li et al. 2018)

claim seedling Tier 1 2026-07-25 · dimensionality, intrinsic-dimension, fine-tuning, LoRA, objective-landscape
Is the 'low-dimensional subspace that constrains adaptation' one shared mathematical object across neural manifolds and LLM intrinsic dimension — or three analogies that share a shape?

capture promoted 2026-07-19 · neural-manifolds, intrinsic-dimension, dimensionality, fine-tuning, LoRA
LoRA shows GPT-3 175B's fine-tuning weight update has intrinsic rank as low as 1 or 2, extending the falling-intrinsic-dimension trend two orders of magnitude past Aghajanyan et al.'s test range

claim seedling Tier 1 2026-07-18 · dimensionality, fine-tuning, intrinsic-dimension, LoRA, scaling
Why does the intrinsic dimension of fine-tuning fall as models get larger — and does that make bigger models geometrically safer to adapt?

capture promoted Tier 1 2026-07-17 · dimensionality, fine-tuning, intrinsic-dimension, scaling, LoRA
Fine-tuning a large language model has very low intrinsic dimension: 200 parameters tune RoBERTa to 90% of full performance on MRPC (Aghajanyan et al. 2020)

claim seedling Tier 1 2026-07-12 · dimensionality, fine-tuning, LoRA, intrinsic-dimension, large-language-models
Why does the intrinsic dimension of fine-tuning fall as models get larger — and does that make bigger models geometrically safer to adapt?

question answered 2026-07-12 · dimensionality, fine-tuning, intrinsic-dimension, scaling, LoRA
Low-dimensional subspaces constrain adaptation in both brains and neural networks

capture promoted Tier 1 2026-07-11 · neural-manifolds, dimensionality, learning, fine-tuning, LoRA