Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training
The paper introduces Hybrid-LoRA, a new framework for post-training large language models. This approach combines full fine-tuning with low-rank adaptation to optimize performance while reducing computational costs. Experiments demonstrate that Hybrid-LoRA achieves performance close to full fine-tuning while being more efficient.
- ▪Hybrid-LoRA selectively applies full fine-tuning to a small subset of modules that are less suited to low-rank adaptation.
- ▪The framework introduces a Hybrid-LoRA Score to rank modules based on their sensitivity to low-rank adaptation.
- ▪Experiments show improvements of up to 5.65% over state-of-the-art parameter-efficient fine-tuning baselines.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18822 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Machine Learning arXiv:2605.18822 (cs) [Submitted on 12 May 2026] Title:Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training Authors:Chengqian Zhang, Wei Zhu, Kyumin Lee View a PDF of the paper titled Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training, by Chengqian Zhang and 2 other authors View PDF HTML (experimental) Abstract:Post-training has become essential for adapting large language models (LLMs) to complex downstream behaviors, including instruction following, preference alignment, and multi-step reasoning.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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