Pith Number

pith:DP3HPN57

pith:2025:DP3HPN57BTGMINZHNGPZNWJRE4

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LoRA-DA: Data-Aware Initialization for Low-Rank Adaptation via Asymptotic Analysis

Chang Chu, Qi Li, Qingyue Zhang, Shao-Lun Huang, Tianren Peng, Xiangyang Luo, Zhihao Jiang

Minimizing the expected parameter discrepancy between fine-tuned and target models yields an optimal data-aware initialization for LoRA.

arxiv:2510.24561 v3 · 2025-10-28 · cs.LG · cs.AI

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Claims

C1strongest claim

Solving this problem yields an optimal initialization strategy for LoRA, based on which we develop an efficient algorithm, LoRA-DA. Empirical results across multiple benchmarks demonstrate that LoRA-DA consistently improves final accuracy over existing initialization methods.

C2weakest assumption

The bias term is approximated using a Fisher-gradient formulation to preserve anisotropy while the variance term uses the Fisher information to capture sampling uncertainty; this approximation must hold for the derived initialization to be optimal in the target fine-tuning regime.

C3one line summary

LoRA-DA derives an optimal data-aware LoRA initialization by solving an optimization problem from asymptotic analysis of parameter discrepancy using Fisher-gradient bias and Fisher-information variance terms.

Formal links

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Receipt and verification

First computed	2026-06-08T01:03:50.386093Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

1bf677b7bf0cccc43727699f96d93127054d573cf321ee2b537bded353e506f1

Aliases

arxiv: 2510.24561 · arxiv_version: 2510.24561v3 · doi: 10.48550/arxiv.2510.24561 · pith_short_12: DP3HPN57BTGM · pith_short_16: DP3HPN57BTGMINZH · pith_short_8: DP3HPN57

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/DP3HPN57BTGMINZHNGPZNWJRE4 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1bf677b7bf0cccc43727699f96d93127054d573cf321ee2b537bded353e506f1

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "f18d6e330c036d5df6c7ececba4202299bf8cb899ec23d89e25de73155c45295",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2025-10-28T15:55:36Z",
    "title_canon_sha256": "b811800cce06bd1d870a26acdd53dc356f65eff51563678282744256ac902ec7"
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    "kind": "arxiv",
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