Revisiting Open-Loop Execution in Robotics: Toward Reactive, Higher-Performing Policies

*Action chunking* — the practice of predicting a sequence of actions and executing a prefix open-loop — has emerged as a key enabler of recent progress in imitation learning for robotic manipulation. However, executing long open-loop prefixes reduces reactivity, limiting policies' ability to correct for errors. Further, the mechanisms underlying these performance benefits remain poorly understood: prior works cite mitigating compounding errors, absorbing inference latency, or smoothing motions, but provide limited controlled evidence or guidance for preserving reactivity. In this work, we argue that long open-loop execution primarily helps short-context policies imitate "non-Markovian demonstrations”. Across four simulation and two real-world tasks, we show that expert non-Markovianity shapes the relationship between task success and open-loop execution horizon. Further, we investigate the impact of compounding errors — the prevailing explanation for long open-loop execution in prior work — and find that while they matter, expert non-Markovianity has a much stronger impact in our experimental setting. Finally, we show that when policies are provided with a sufficiently long context in the high data regime, open-loop execution is no longer beneficial and the most reactive, closed-loop policies perform best. Overall, our findings motivate long-context, reactive policies as a more principled and performant paradigm. Website: https://open-loop-action-chunking.netlify.app/