JumpLoRA Framework Introduces Sparse Adapters for Continual Learning in Large Language Models
A new framework named JumpLoRA has been introduced to improve continual learning in large language models via sparse adapters. Utilizing JumpReLU gating, this technique creates sparsity within Low-Rank Adaptation blocks, allowing for dynamic parameter isolation that avoids task interference. This modular method integrates seamlessly with existing LoRA-based continual learning strategies. Notably, JumpLoRA enhances the performance of IncLoRA and surpasses the top state-of-the-art continual learning technique, ELLA. Adapter-based methods offer a cost-efficient solution for continual learning in LLMs by sequentially acquiring low-rank update matrices for each task. To address catastrophic forgetting, current strategies apply constraints on new adapters in relation to earlier ones, focusing on either subspace or coordinate-wise interference. The study was published on arXiv under the identifier 2604.16171.
Key facts
- JumpLoRA is a novel framework for continual learning in large language models
- It uses JumpReLU gating to induce sparsity in LoRA blocks
- The method achieves dynamic parameter isolation to prevent task interference
- JumpLoRA is modular and compatible with LoRA-based continual learning approaches
- It significantly boosts the performance of IncLoRA
- JumpLoRA outperforms the state-of-the-art continual learning method ELLA
- Adapter-based methods are cost-effective for continual learning in LLMs
- The research was published on arXiv with identifier 2604.16171
Entities
Institutions
- arXiv
- arXivLabs