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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.02216 |
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| _version_ | 1866911439051030528 |
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| author | Lou, Yuchen Ye, Zeqi Chen, Minshuo |
| author_facet | Lou, Yuchen Ye, Zeqi Chen, Minshuo |
| contents | This paper develops a new perspective on parameter-efficient fine-tuning (PEFT) for LLMs, inspired by classical subspace minimization. We introduce a unifying framework, Parameter-Efficient Subspace Optimization (PESO), which recovers existing methods such as LoRA and connects them to the principled algorithmic and theoretical foundations of subspace optimization. This connection highlights a natural ``exploration--exploitation'' view of subspace methods, guiding the design of new algorithms that achieve strong convergence performance while still preserving memory efficiency. We instantiate the framework into a practical algorithm, PESO-LoRA, based on a LoRA-type parameterization. Importantly, we provide convergence guarantees stated in the full-parameter space for the induced update, addressing a key limitation of LoRA-style analyses that only track low-dimensional factors. Empirically, PESO-LoRA improves over strong PEFT baselines on standard fine-tuning benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_02216 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parameter-Efficient Subspace Optimization for LLM Fine-Tuning Lou, Yuchen Ye, Zeqi Chen, Minshuo Optimization and Control This paper develops a new perspective on parameter-efficient fine-tuning (PEFT) for LLMs, inspired by classical subspace minimization. We introduce a unifying framework, Parameter-Efficient Subspace Optimization (PESO), which recovers existing methods such as LoRA and connects them to the principled algorithmic and theoretical foundations of subspace optimization. This connection highlights a natural ``exploration--exploitation'' view of subspace methods, guiding the design of new algorithms that achieve strong convergence performance while still preserving memory efficiency. We instantiate the framework into a practical algorithm, PESO-LoRA, based on a LoRA-type parameterization. Importantly, we provide convergence guarantees stated in the full-parameter space for the induced update, addressing a key limitation of LoRA-style analyses that only track low-dimensional factors. Empirically, PESO-LoRA improves over strong PEFT baselines on standard fine-tuning benchmarks. |
| title | Parameter-Efficient Subspace Optimization for LLM Fine-Tuning |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2512.02216 |