ResearchResearch paperLarge Language Models · Reinforcement Learning · Robotics & Embodied AI1 source · Oct 14, 2026

LAGO: Language Agents with Reusable Growing Options

We introduce LAGO, which treats specialized language agents as reusable temporally extended options.

Long-horizon tasks expose a weakness of monolithic tool-use loops: one agent must decompose, execute, and verify a task within a single trajectory.
A meta-agent retrieves options from a persistent library or spawns new ones, dispatches sub-tasks, verifies results, and admits successful novel options.
On a fixed 150-task subset, we re-run mini-SWE-agent, Terminus 2, and LAGO with the same policy, temperature, task IDs, and evaluator; their resolve rates are 49.3%, 57.3%, and 60.3%, respectively.
These results support reusable agent-options as a practical abstraction for task streams, while leaving component-level causality task- and model-dependent.

[1]LAGO: Language Agents with Reusable Growing Options
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 09:12 AM
We introduce LAGO, which treats specialized language agents as reusable temporally extended options.
Long-horizon tasks expose a weakness of monolithic tool-use loops: one agent must decompose, execute, and verify a task within a single trajectory.

Extractive summary: sentences quoted from the sources.

Before this