Not Just Faster: Learning from Mixed-Speed Demonstrations

Demonstrations teach robot policies both task behavior and its timing. Can faster training experience improve task completion without changing deployment settings? Retiming changes how much of a reference trajectory falls within a fixed action window, motivating training on both original and accelerated experience. We investigate this idea through an empirical study of vision-language-action and world-action models across simulated and physical manipulation. Our comparisons distinguish changes in training configuration from changes in a trained policy's speed condition or execution schedule. On CALVIN, mixed-speed configurations improve completion for both model families under the normal condition and unchanged execution settings. Execution sweeps further show that stronger chunk compression can reduce completion and increase control steps among successful trials. These findings support treating demonstration pace as a choice in training supervision, while assessing deployment pace separately through its task-dependent effects on success and execution cost.