The Self-Learning Agent with a Progressive Neural Network Integrated Transformer
This is an incremental improvement for AI systems requiring scalable and efficient continual learning.
The paper tackles the problem of continual learning in conversational AI and code generation by integrating LLaMA 3.2 with a Progressive Neural Network, resulting in improved adaptability and memory stability as demonstrated experimentally.
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).