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We introduce Lumos, a novel framework for training language agents thatemploys a unified data format and a modular architecture based on open-sourcelarge language models (LLMs). Lumos consists of three distinct modules:planning, grounding, and execution. The planning module breaks down a task intoa series of high-level, tool-agnostic subgoals, which are then made specific bythe grounding module through a set of low-level actions. These actions aresubsequently executed by the execution module, utilizing a range ofoff-the-shelf tools and APIs. In order to train these modules effectively,high-quality annotations of subgoals and actions were collected and are madeavailable for fine-tuning open-source LLMs for various tasks such as complexquestion answering, web tasks, and math problems. Leveraging this unified dataand modular design, Lumos not only achieves comparable or superior performanceto current, state-of-the-art agents, but also exhibits several key advantages:(1) Lumos surpasses GPT-4/3.5-based agents in complex question answering andweb tasks, while equalling the performance of significantly larger LLM agentson math tasks; (2) Lumos outperforms open-source agents created throughconventional training methods and those using chain-of-thoughts training; and(3) Lumos is capable of effectively generalizing to unseen interactive tasks,outperforming larger LLM-based agents and even exceeding performance ofspecialized agents.

Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs