Cooperative Fine Tuning

Cooperative fine-tuning focuses on collaboratively adapting large language models (LLMs) and other foundation models to specific tasks across multiple devices or agents, improving efficiency and performance compared to single-device fine-tuning. Current research emphasizes efficient resource management strategies, including distributed optimization algorithms and techniques like Low-Rank Adaptation (LoRA) to reduce communication overhead, particularly in scenarios with limited data or computational resources. This approach is significant for enabling the deployment of powerful models on resource-constrained devices and improving the ethical considerations of model training by allowing for collaborative refinement of model behavior based on diverse datasets.