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

• URL: http://arxiv.org/abs/2607.16252v1
• Date: Sat, 27 Jun 2026 03:25:36 GMT
• Title: SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling
• Abstract summary: Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning (PEFT) method for large language models.<n>We propose Static Orthogonal Subspace LoRA (SOS-LoRA), a drop-in extension that re parameterizes a rank-rtot update as a sum of K static experts.
• Score: 17.873016968298483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning (PEFT) method for large language models. Under a fixed rank budget, LoRA parameterizes each adapted weight through a single low-dimensional input-side pathway, which may couple heterogeneous behaviors through shared input directions and induce interference during optimization. We propose Static Orthogonal Subspace LoRA (SOS-LoRA), a drop-in extension that reparameterizes a rank-rtot update as a sum of K static (always-on, non-routed) low-rank experts. SOS-LoRA (i) decomposes the total rank across experts, (ii) applies a fixed multi-scale scaling scheme to encourage scale-separated optimization dynamics, and (iii) promotes diverse input-side directions via cross-expert orthogonal initialization and a lightweight regularizer. SOS-LoRA remains fully mergeable, adding no inference-time parameters or latency after merging. Experiments on reasoning and knowledge-intensive benchmarks (Llama 2/3), encoder-based NLU (GLUE), and math reasoning (GSM8K/MATH) show consistent gains over matched-budget LoRA baselines and recent variants. Code is available at https://github.com/llm172/sos-lora.

Related papers

• Balanced LoRA: Removing Parameter Invariance to Accelerate Convergence [30.96331195818476]
  Low-Rank Adaptation (LoRA) is the most widely adopted method for fine-tuning large language models.<n>We introduce Balanced Low-Rank Adaptation (BaLoRA), a variant of LoRA that projects iterates onto a balanced manifold.<n>BaLoRA converges faster than standard LoRA and achieves superior performance across a range of fine-tuning tasks.
  arXiv  Detail & Related papers  (2026-05-29T16:08:28Z)
• SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning [57.85676271833619]
  Low-rank Adaptation (LoRA) uses a low-rank update method to simulate full parameter fine-tuning.<n>As the rank increases, more principal singular directions are preserved, which generally improves the model's performance.<n>We propose SMoA, a textbfSpectrum textbfModulation textbfAdapter that enlarges the accessible family of spectrum-aware updates.
  arXiv  Detail & Related papers  (2026-05-20T13:19:28Z)
• Strategic Over-Parameterization for Generalizable Low-Rank Adaptation [14.867641913391779]
  Adapting large language models to downstream tasks via full fine-tuning is increasingly impractical due to its computational and memory demands.<n>We introduce LoRA-Over, a framework grounded in a simple principle: enrich the optimization landscape during training, then collapse the enrichment at inference.<n>We evaluate LoRA-Over on language understanding (GLUE, T5-Base), dialogue (MT-Bench), arithmetic reasoning (GSM8K), and code generation (HumanEval) using LLaMA 2-7B and LLaMA 3.1-8B.
  arXiv  Detail & Related papers  (2026-05-15T12:26:17Z)
• Post-Optimization Adaptive Rank Allocation for LoRA [10.796887002907456]
  Post-Optimization Adaptive Rank Allocation (PARA) is a data-free compression method for Low-Rank Adaptation (LoRA)<n>We propose PARA, a data-free compression method for LoRA that integrates seamlessly into existing fine-tuning pipelines.<n>We empirically demonstrate that PARA reduces parameter count by 75-90% while preserving the predictive performance of the original, uncompressed LoRA across vision and language benchmarks.
  arXiv  Detail & Related papers  (2026-04-30T12:40:11Z)
• Beyond SGD, Without SVD: Proximal Subspace Iteration LoRA with Diagonal Fractional K-FAC [50.36542772932594]
  Low-Rank Adaptation (LoRA) fine-tunes large models by learning low-rank updates on top of frozen weights.<n>In this work, we address the gap between training with full steps with low-rank projections (SVDLoRA) and LoRA fine-tuning.<n>We propose LoRSum, a memory-efficient subroutine that closes this gap for gradient descent.
  arXiv  Detail & Related papers  (2026-02-18T13:41:41Z)
• Faster Than SVD, Smarter Than SGD: The OPLoRA Alternating Update [50.36542772932594]
  Low-Rank Adaptation (LoRA) fine-tunes large models by learning low-rank updates on top of frozen weights.<n>There is still a gap between full training with low-rank projections (SVDLoRA) and LoRA fine-tuning, indicating that LoRA steps can be further improved.
  arXiv  Detail & Related papers  (2025-09-24T10:32:50Z)
• SRLoRA: Subspace Recomposition in Low-Rank Adaptation via Importance-Based Fusion and Reinitialization [2.594346658179846]
  Low-Rank Adaptation (LoRA) constrains updates to a fixed low-rank subspace.<n>We introduce Subspace Recomposition in Low-Rank Adaptation (SRLoRA) via importance-based fusion and reinitialization.<n> SRLoRA consistently achieves faster convergence and improved accuracy over standard LoRA.
  arXiv  Detail & Related papers  (2025-05-18T14:12:40Z)
• Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation [58.288682735160585]
  Low-Rank Adaptation (LoRA) is a popular technique for finetuning models. LoRA often under performs when compared to full- parameter fine-tuning. We present a framework that rigorously analyzes the adaptation rates of LoRA methods.
  arXiv  Detail & Related papers  (2024-10-10T18:51:53Z)
• Flat-LoRA: Low-Rank Adaptation over a Flat Loss Landscape [52.98187034726091]
  We introduce Flat-LoRA, which aims to identify a low-rank adaptation situated in a flat region of the full parameter space.<n>We show that Flat-LoRA improves both in-domain and out-of-domain generalization.
  arXiv  Detail & Related papers  (2024-09-22T11:24:10Z)
• LoRA-Pro: Are Low-Rank Adapters Properly Optimized? [121.0693322732454]
  Low-rank adaptation, also known as LoRA, has emerged as a prominent method for parameter-efficient fine-tuning of foundation models.<n>Despite its computational efficiency, LoRA still yields inferior performance compared to full fine-tuning.<n>We introduce LoRA-Pro, a method that enhances LoRA's performance by strategically adjusting the gradients of low-rank matrices.
  arXiv  Detail & Related papers  (2024-07-25T17:57:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.