LearnAct
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LearnAct lets language agents expand and refine their own action space over time by writing and revising Python functions in response to execution feedback.
Open-action learning: Rather than picking from a fixed list of actions, the agent proposes new actions as Python functions, tests them, and iteratively revises them based on what worked.
Iterative refinement loop: Each cycle adds or updates actions using execution feedback, so the agent's effective toolkit grows along with task experience.
Strong results on AlfWorld: Achieves a 32% absolute improvement over ReAct+Reflexion on AlfWorld, with additional gains on robotic planning tasks.
Closer to human learning: Mirrors the way humans acquire new skills by composing and revising procedures rather than selecting from a static repertoire, pointing toward more capable long-horizon agents.
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