LearnAct

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.