allocate-lora-rank-per-network-region-pre-training TEC-0239 · proposedAllocate LoRA rank per network region by a pre-training diag
From ten unlabeled target observations, score each functional region of a VLA by combining CKA against a cached source reference with reference-free gradient and MC-dropout sensitivity, then set per-region LoRA rank proportional to the scor
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
From ten unlabeled target observations, score each functional region of a VLA by combining CKA against a cached source reference with reference-free gradient and MC-dropout sensitivity, then set per-region LoRA rank proportional to the scor Filed by the drafting stage from Not All Layers Need Tuning: Diagnosing and Directing Adaptation in Vision-Language-Action Models (2609.18084) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Diagnostic scores ranked regions against measured region-isolated parameter displacement at median Spearman 0.91; at a matched budget of roughly half uniform rank-32, the allocation beat uniform-matched LoRA, DoRA, (IA)^3, AdaLoRA, LoRA-SP, random and dominant-region-only allocation on all five architectures on LIBERO and CALVIN (5 seeds, 50 rollouts/task), and on the xArm-7 language shift reached 21/30 vs 22/30 for full fine-tuning at 0.04% of its trainable parameters; random allocation at the same budget collapsed to 42.8% vs 73.4%.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
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