Papers › Mixture-of-Subspaces in Low-Rank Adaptation
Mixture-of-Subspaces in Low-Rank Adaptation
Taiqiang Wu, Jiahao Wang, Zhe Zhao, Ngai Wong
In this paper, we introduce a subspace-inspired Low-Rank Adaptation (LoRA) method, which is computationally efficient, easy to implement, and readily applicable to large language, multimodal, and diffusion models. Initially, we equivalently decompose the weights of LoRA into two subspaces, and find that simply mixing them can enhance performance. To study such a phenomenon, we revisit it through a fine-grained subspace lens, showing that such modification is equivalent to employing a fixed mixer to fuse the subspaces. To be more flexible, we jointly learn the mixer with the original LoRA weights, and term the method Mixture-of-Subspaces LoRA (MoSLoRA). MoSLoRA consistently outperforms LoRA on tasks in different modalities, including commonsense reasoning, visual instruction tuning, and subject-driven text-to-image generation, demonstrating its effectiveness and robustness. Codes are available at https://github.com/wutaiqiang/MoSLoRA.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Common Sense Reasoning | ARC (Challenge) | LLaMA 3 8B + MoSLoRA (fine-tuned) | Accuracy | 81.5 | #16 of 54 | Archive leaderboard | report |
| Common Sense Reasoning | ARC (Easy) | LLaMA 3 8B+MoSLoRA (fine-tuned) | Accuracy | 90.5 | #2 of 47 | Archive leaderboard | report |
| Common Sense Reasoning | WinoGrande | LLaMA3 8B+MoSLoRA | Accuracy | 85.8 | #10 of 77 | Archive leaderboard | report |
| Question Answering | BoolQ | LLaMA3+MoSLoRA | Accuracy | 74.6 | #38 of 65 | Archive leaderboard | report |
| Question Answering | OpenBookQA | LLaMA-3 8B+MoSLoRA | Accuracy | 86.8 | #14 of 45 | Archive leaderboard | report |
| Question Answering | PIQA | LLaMA3 8B+MoSLoRA | Accuracy | 89.7 | #2 of 67 | Archive leaderboard | report |
| Question Answering | SIQA | LLaMA-3 8B+MoSLoRA (fine-tuned) | Accuracy | 81.0 | #5 of 24 | Archive leaderboard | report |
| Sentence Completion | HellaSwag | LLaMA3+MoSLoRA | Accuracy | 95.0 | #5 of 89 | Archive leaderboard | report |
| Visual Question Answering | MM-Vet | LLaVA-InternLM2-7B-ViT + MoSLoRA | GPT-4 score | 35.2 | #159 of 231 | Archive leaderboard | report |
| Visual Question Answering | MM-Vet | InternLM2+ViT (QMoSLoRA) | GPT-4 score | 35.2 | #160 of 231 | Archive leaderboard | report |
| Visual Question Answering | MMBench | LLaVA-InternLM2-ViT + MoSLoRA | GPT-3.5 score | 73.8 | #1 of 5 | Archive leaderboard | report |
| Visual Question Answering | MMBench | LLaVA-LLaMA3-8B-ViT + MoSLoRA | GPT-3.5 score | 73.0 | #3 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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