This paper introduces a self-learning agent that integrates LLaMA 3.2 with a Progressive Neural Network (PNN) for continual learning in conversational AI and code generation. The framework dynamically collects data, fine-tunes tasks with minimal samples, and leverages Meta-Learning for rapid adaptation. LoRA optimizes fine-tuning, while Elastic Weight Consolidation (EWC) enhances knowledge retention. Experimental results demonstrate improved adaptability and memory stability, positioning this approach as a scalable step toward Artificial General Intelligence (AGI).
@article{arxiv.2504.02489,
title = {The Self-Learning Agent with a Progressive Neural Network Integrated Transformer},
author = {Ajay Sivakumar and Shalini and Vasantha Raj and Sebastian Sylvester},
journal= {arXiv preprint arXiv:2504.02489},
year = {2025}
}
Comments
7 pages, 2 figures, focuses on continual learning with PNN and LLaMA. Experiments demonstrate scalability and lifelong learning capabilities