Agent Lumos
Lumos is a unified recipe for training open-source LLM agents that separates high-level planning from low-level grounding so each module can be supervised and improved independently.
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Modular architecture: One module learns to decompose complex tasks into subgoals (planning), while a second module translates those subgoals into concrete tool calls and actions (grounding).
Training data: The authors compile large-scale agent annotations by re-formatting reasoning rationales from math, web, and QA tasks into the Lumos planner/grounder format.
Nine-dataset evaluation: Across complex QA, web tasks, and math tasks, Lumos beats larger open agents and even surpasses GPT-3.5/GPT-4-based agents on QA and web domains.
Takeaway: Explicit modularization gives open-source agents a reproducible way to catch up to closed-source systems without needing frontier-scale base models.