  Closed-source agents suffer from several issues such as a lack of
affordability, transparency, and reproducibility, particularly on complex
interactive tasks. This motivates the development of open-source alternatives.
We introduce LUMOS, one of the first frameworks for training open-source
LLM-based agents. LUMOS features a learnable, unified, and modular architecture
with a planning module that learns high-level subgoal generation, and a
grounding module trained to translate these into actions using various tools in
the execution module. The design allows for modular upgrades and wider
applicability to diverse interactive tasks. To foster generalizable agent
learning, we collect large-scale, unified, and high-quality training
annotations derived from diverse ground-truth reasoning rationales across
various complex interactive tasks. On 9 datasets, LUMOS exhibits several key
advantages: (1) LUMOS excels multiple larger open-source agents on the held-out
datasets (unused for training) for each task type. LUMOS even surpasses GPT
agents on QA and web tasks; (2) LUMOS outperforms open-source agents produced
by chain-of-thoughts and unmodularized integrated training; and (3) LUMOS
effectively generalizes to unseen tasks, outperforming 33B-scale agents and
domain-specific agents.
