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NeuroLoRA: Context-Aware Neuromodulation for Parameter-Efficient Multi-Task Adaptation

arXiv:2603.12378v1 Announce Type: cross Abstract: Parameter-Efficient Fine-Tuning (PEFT) techniques, particularly Low-Rank Adaptation (LoRA), have become essential for adapting Large Language Models (LLMs) to downstream tasks. While the recent FlyLoRA framework successfully leverages bio-inspired sparse random projections to mitigate parameter interference, it relies on a static, magnitude-based routing mechanism that is agnostic to input context. In this paper, we propose NeuroLoRA, a novel Mixture-of-Experts (MoE) based LoRA framework inspired by biological neuromodulation -- the dynamic regulation of neuronal excitability based on context. NeuroLoRA retains the computational efficiency of frozen random projections while introducing a lightweight, learnable neuromodulation gate that contextually rescales the projection space prior to expert selection. We further propose a Contrastive Orthogonality Loss to explicitly enforce separation between expert subspaces, enhancing both task de

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Yuxin Yang, Haoran Zhang, Mingxuan Li, Jiachen Xu, Ruoxi Shen, Zhenyu Wang, Tianhao Liu, Siqi Chen, Weilin Huang · · 1 min read · 8 views
#cs.LG #cs.CL

arXiv:2603.12378v1 Announce Type: cross Abstract: Parameter-Efficient Fine-Tuning (PEFT) techniques, particularly Low-Rank Adaptation (LoRA), have become essential for adapting Large Language Models (LLMs) to downstream tasks. While the recent FlyLoRA framework successfully leverages bio-inspired sparse random projections to mitigate parameter interference, it relies on a static, magnitude-based routing mechanism that is agnostic to input context. In this paper, we propose NeuroLoRA, a novel Mixture-of-Experts (MoE) based LoRA framework inspired by biological neuromodulation -- the dynamic regulation of neuronal excitability based on context. NeuroLoRA retains the computational efficiency of frozen random projections while introducing a lightweight, learnable neuromodulation gate that contextually rescales the projection space prior to expert selection. We further propose a Contrastive Orthogonality Loss to explicitly enforce separation between expert subspaces, enhancing both task decoupling and continual learning capacity. Extensive experiments on MMLU, GSM8K, and ScienceQA demonstrate that NeuroLoRA consistently outperforms FlyLoRA and other strong baselines across single-task adaptation, multi-task model merging, and sequential continual learning scenarios, while maintaining comparable parameter efficiency.

Executive Summary

The article introduces NeuroLoRA, a novel framework for parameter-efficient multi-task adaptation of Large Language Models (LLMs). NeuroLoRA builds upon Low-Rank Adaptation (LoRA) and incorporates a learnable neuromodulation gate inspired by biological neuromodulation, allowing for contextual rescaling of the projection space. This approach enhances task decoupling and continual learning capacity, outperforming existing baselines in various scenarios while maintaining parameter efficiency.

Key Points

  • ▸ NeuroLoRA introduces a learnable neuromodulation gate for contextual rescaling of the projection space
  • ▸ The framework incorporates a Contrastive Orthogonality Loss to enforce separation between expert subspaces
  • ▸ Extensive experiments demonstrate NeuroLoRA's superior performance across single-task adaptation, multi-task model merging, and sequential continual learning scenarios

Merits

Improved Task Decoupling

NeuroLoRA's contextual neuromodulation gate and Contrastive Orthogonality Loss enable more effective task decoupling, reducing parameter interference and improving overall performance

Demerits

Increased Computational Complexity

The introduction of the learnable neuromodulation gate may add computational overhead, potentially impacting the framework's efficiency in certain scenarios

Expert Commentary

The introduction of NeuroLoRA represents a significant advancement in the field of parameter-efficient multi-task adaptation. By incorporating a learnable neuromodulation gate and Contrastive Orthogonality Loss, the framework demonstrates improved task decoupling and continual learning capacity. However, further research is needed to fully explore the potential applications and limitations of NeuroLoRA, particularly in scenarios where computational resources are constrained. As the field continues to evolve, it is likely that NeuroLoRA will play a key role in the development of more efficient and adaptable LLMs.

Recommendations

  • ✓ Future research should focus on exploring the applications of NeuroLoRA in various natural language processing tasks and scenarios
  • ✓ Further investigation is needed to optimize the computational efficiency of NeuroLoRA and minimize potential overhead

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