Action chunking has recently emerged as a standard practice in flow-based Vision-Language-Action (VLA) models. However, the effect and choice of the execution horizon - the number of actions to be executed from each predicted chunk - remains underexplored. In this work, we first show that varying the execution horizon leads to substantial performance deviations, with performance initially improving and then declining as the horizon increases. To uncover the reasons, we analyze the cross- and self-attention weights in flow-based VLAs and reveal two key phenomena: (i) intra-chunk actions attend invariantly to vision-language tokens, limiting adaptability to environmental changes; and (ii) the initial and terminal action tokens serve as stable anchors, forming latent centers around which intermediate actions are organized. Motivated by these insights, we interpret action self-attention weights as a proxy for the model's predictive limit and propose AutoHorizon, the first test-time method that dynamically estimates the execution horizon for each predicted action chunk to adapt to changing perceptual conditions. Across simulated and real-world robotic manipulation tasks, AutoHorizon is performant, incurs negligible computational overhead, and generalizes across diverse tasks and flow-based models.
Figures & tables
Figure 1: Illustration of the average success rates on the LIBERO benchmark using π0.5 . Varying the execution horizon leads to substantial success rate fluctuations, and the policy performance exhibits a peaked pattern, initially improving and then declining as the execution horizon increases.
Figure 2: Left: (a) In conventional action chunking, the execution horizon e is heuristically chosen by humans and remains fixed across chunks. (b) In contrast, AutoHorizon (our method) dynamically estimates the execution horizon for each predicted chunk based on the attention weights from the VLA model. Right: Real-world demonstration of showing how the estimated execution horizons evolve during policy rollout. When the environment is stable and reactivity is less critical ( e.g . , reaching the cube or moving toward the bowl), the estimated horizon increases to promote smooth, stable motion. Conversely, during physical interaction ( e.g . , grasping or placing the cube), the execution horizon shortens to enhance reactivity and adaptability.
Figure 3: Visualization of average attention weights in π0.5 across different stages of task execution. Intra-chunk actions consistently attend to the same vision and language tokens across predicted chunks throughout the rollout. This invariance is consistently observed across different sampling steps, task rollouts, and pretrained models. The x-axis is rescaled for clarity of visualization.
Figure 4: Visualization of normalized action self-attention weights. Across different prediction horizons, the predicted actions exhibit strong attention to the initial and terminal action tokens, with correspondence strength remaining high before sharply decaying as temporal distance increases. These boundary tokens (encircled in black) are referred to as radial action sinks .
Figure 5: Estimated execution horizon distributions by AutoHorizon. The legend displays the mean values of the distributions.
Figure 6: Example demonstration of how AutoHorizon works.
Figure 7: Average success rates on the LIBERO benchmark with a prediction horizon of 10 using π0.5 .
Figure 8: Visualization of the cross-attention weights in GR00T N1.5 over different rollout steps.
Figure 9: Smoothness analysis.
Figure 10: Visualization of normalized action self-attention in GR00T N1.5 over different rollout steps.