Humans often take longer to demonstrate a task than a robot would need to execute it. Rather than learning to replicate the demonstration at the same pace,
many industrial and practical applications require robots to perform tasks as quickly as possible. In this paper, we investigate several hypotheses for learning
policies that operate \textit{faster-than-demonstrations}. Our experiments show that the most effective strategy is to downsample recorded demonstrations and
train the robot's policy on this accelerated data. However, uniformly downsampling an entire trajectory can be problematic. Some parts of a task can be safely
sped up (e.g., unconstrained motion), while others demand slower, more precise motion (e.g., object interactions or fine manipulation). To address this challenge,
we introduce VOLT, a vision-and-language trajectory segmentation method that reasons over video demonstrations, and leverages contextual cues to determine when
acceleration is appropriate and when careful precision is required. VOLT identifies segments where slow, deliberate motion is necessary, then selectively downsamples
the remaining segments. The resulting reformatted trajectories can be used with standard imitation learning approaches, such as diffusion policies. Our results highlight
that segmentation quality is critical---baseline methods often misidentify when acceleration is possible, leading to overly cautious or unreliable policies.
Compared to state-of-the-art alternatives, VOLT allows robots to execute tasks faster while maintaining strong performance.
