The Internet of Large Language Models: An Orchestration Framework for LLM Training and Knowledge Exchange Toward Artificial General Intelligence
This addresses resource and scalability problems for LLM researchers and developers, but it appears incremental as it builds on existing concepts like sharing and optimization.
This paper tackles the challenges in developing Large Language Models (LLMs), such as massive scale, high computational costs, and limited functionality, by proposing an orchestration framework with three technical solutions and a joint mining mechanism. The result is a system that provides cost-optimized computational support and promotes LLM research, though no concrete performance numbers are provided.
This paper explores the multi-dimensional challenges faced during the development of Large Language Models (LLMs), including the massive scale of model parameters and file sizes, the complexity of development environment configuration, the singularity of model functionality, and the high costs of computational resources. To address these challenges, this paper proposes three core technical solutions: LLM sharing protocol, LLM universal environment framework, and Agent optimal path module. To solve the computational resource constraints in the early stages of research, we further innovatively propose a joint mining mechanism, achieving bilateral value sharing between computing power providers and model designers, including breakthrough rewards for optimal model paths and long-term profit distribution, thereby providing researchers with cost-optimized computational resource support and promoting the continuous development of LLM research and applications.