PALoRA: Projection-Adaptive LoRA for Preserving Reasoning in Large Language Models
The paper introduces PALoRA, a framework designed to enhance the integration of new knowledge into Large Language Models while preserving their reasoning capabilities. It addresses the challenge of maintaining reasoning performance amidst factual updates by employing a two-stage process. The results demonstrate that PALoRA retains a high level of reasoning performance while ensuring competitive factual recall across various benchmarks.
- ▪PALoRA stands for Projection-Adaptive LoRA and aims to tackle the plasticity-stability dilemma in Large Language Models.
- ▪The framework utilizes a Singular Value Fine-Tuning expert to identify critical components for reasoning before injecting new factual knowledge.
- ▪PALoRA has been shown to preserve 95% of reasoning performance while adding minimal parameter overhead.
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Computer Science > Artificial Intelligence arXiv:2605.24549 (cs) [Submitted on 23 May 2026] Title:PALoRA: Projection-Adaptive LoRA for Preserving Reasoning in Large Language Models Authors:Mustafa Hayri Bilgin, Mariam Barry, Albert Bifet, Azzedine Idir Ait Said, Soumya Banerjee View a PDF of the paper titled PALoRA: Projection-Adaptive LoRA for Preserving Reasoning in Large Language Models, by Mustafa Hayri Bilgin and 4 other authors View PDF HTML (experimental) Abstract:Efficiently updating Large Language Models (LLMs) with new or evolving factual knowledge remains a central challenge, as even parameter-efficient adaptation can erode previously acquired reasoning abilities.
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