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OLoRA: Orthonormal Low-Rank Adaptation of Large Language Models

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arxiv 2406.01775 v2 pith:2O5C7HHM submitted 2024-06-03 cs.CL

Kerim Büyükakyüz This is my paper · ORCID
classification cs.CL
keywords languageloraolorafine-tuningmodelsadaptationconvergenceefficient
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The advent of large language models (LLMs) has revolutionized natural language processing, enabling unprecedented capabilities in understanding and generating human-like text. However, the computational cost and convergence times associated with fine-tuning these models remain significant challenges. Low-Rank Adaptation (LoRA) has emerged as a promising method to mitigate these issues by introducing efficient fine-tuning techniques with a reduced number of trainable parameters. In this paper, we present OLoRA, an enhancement to the LoRA method that leverages orthonormal matrix initialization through QR decomposition. OLoRA significantly accelerates the convergence of LLM training while preserving the efficiency benefits of LoRA, such as the number of trainable parameters and GPU memory footprint. Our empirical evaluations demonstrate that OLoRA not only converges faster but also exhibits improved performance compared to standard LoRA across a variety of language modeling tasks. This advancement opens new avenues for more efficient and accessible fine-tuning of LLMs, potentially enabling broader adoption and innovation in natural language applications.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ST-LoRA: Single Trajectory LoRA Ensemble for Uncertainty Aware Agricultural Segmentation

    cs.CV 2026-08 conditional novelty 6.0 of 10

    Combining LoRA with snapshot ensembling yields a parameter-efficient uncertainty-aware segmentation ensemble that matches snapshot full-rank baselines, with feed-forward layers identified as the critical LoRA target.

  2. Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models

    cs.LG 2026-01 reject novelty 6.0 of 10

    SALR combines static pruning of frozen weights with a trainable truncated-SVD low-rank residual adapter to match LoRA accuracy at 50% sparsity, cutting model size ~2x and giving ~1.7x inference speedup.

  3. ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints

    cs.CV 2025-07 conditional novelty 6.0 of 10

    LoRA adapters can be initialized with a closed-form estimate derived from constraint sets linking source and target activations, improving fine-tuning speed and accuracy.

  4. HRP: High-Rank Preheating for Superior LoRA Initialization

    cs.LG 2025-02 conditional novelty 6.0 of 10

    HRP initializes LoRA with the top singular vectors of a briefly preheated high-rank adapter, improving fine-tuning results over random initialization in experiments.

  5. CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization

    cs.LG 2025-01 conditional novelty 6.0 of 10

    CLoQ initializes LoRA adapters on quantized LLMs with a closed-form calibration-aware low-rank solution, improving 2-bit fine-tuning accuracy.

  6. OP-LoRA: The Blessing of Dimensionality

    cs.LG 2024-12 conditional novelty 6.0 of 10

    Predicting LoRA adapter weights with a small MLP, then discarding the MLP after training, improves fine-tuning performance across tasks at zero extra inference cost.

  7. Unsupervised Adaptation of PDE Foundation Models

    cs.AI 2026-08 conditional novelty 5.0 of 10

    A pretrained PDE foundation model fine-tuned with only PDE residuals and boundary conditions reaches within about 2.5x of supervised fine-tuning on seven of eight 2D benchmarks.

  8. SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling

    cs.LG 2026-06 conditional novelty 5.0 of 10

    A LoRA update split into several fixed, differently-scaled low-rank experts with orthogonal input directions improves fine-tuning accuracy at the same parameter count.

  9. QR-LoRA: QR-Based Low-Rank Adaptation for Efficient Fine-Tuning of Large Language Models

    cs.LG 2025-08 conditional novelty 5.0 of 10

    QR-LoRA trains only scalar coefficients on a pivoted QR basis of pretrained weights, reportedly matching full fine-tuning on GLUE with 601 parameters.

  10. Three-Pronged Spectral Control for Federated Parameter Efficient Fine Tuning

    cs.LG 2026-07 conditional novelty 4.0 of 10

    A federated fine-tuning method combining shared multi-head low-rank bases, singular-value shrinkage, and layer-wise budget allocation to improve accuracy under non-IID client data.

  11. Dual Decomposition of Weights and Singular Value Low Rank Adaptation

    cs.CL 2025-05 conditional novelty 4.0 of 10

    DuDe combines DoRA's magnitude-direction decomposition with PiSSA's SVD-based initialization, reporting consistent but modest accuracy gains over LoRA, DoRA, and PiSSA on commonsense reasoning, GPQA, MMLU, and GSM8K.

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