The Infinite Tool Use Paradigm for LLMs

This article proposes a novel paradigm for Large Language Models (LLMs): infinite tool use. The paradigm suggests that LLMs should only output tool calls and their arguments, breaking down complex tasks into a series of tool calls. This avoids the context window limitations and error accumulation problems traditional LLMs face when handling long texts and complex tasks. Through external tools (like text editors, CAD software, etc.), LLMs can perform multi-level text generation, 3D modeling, and more, effectively managing contextual information. This approach not only improves LLM efficiency and accuracy but also enhances safety, as models must use tools clearly to accomplish complex tasks, reducing deceptive outputs. Training relies primarily on reinforcement learning, leveraging the 'forgetfulness' of LLMs to address infinite context length challenges.