Adaptive Action Chunking at Inference-time for Vision-Language-Action Models

Abstract

In Vision-Language-Action (VLA) models, action chunking (i.e., executing a sequence of actions without intermediate replanning) is a key technique to improve robotic manipulation abilities. However, a large chunk size reduces the model's responsiveness to new information, while a small one increases the likelihood of mode-jumping, jerky behavior resulting from discontinuities between chunks. Therefore, selecting the optimal chunk size is an urgent demand to balance the model's reactivity and consistency. Unfortunately, a dominant trend in current VLA models is an empirical fixed chunk length at inference-time, hindering their superiority and scalability across diverse manipulation tasks. To address this issue, we propose a novel Adaptive Action Chunking (AAC) strategy, which exploits action entropy as the cue to adaptively determine the chunk size based on current predictions. Extensive experiments on a wide range of simulated and real-world robotic manipulation tasks have demonstrated that our approach substantially improves performance over the state-of-the-art alternatives. The videos and source code are publicly available at https://lance-lot.github.io/adaptive-chunking.github.io/.

Figures (6)

• Figure 1: Effects of action chunk sizes. At inference-time, the success rates of the GR00T N1.5 bjorck2025gr00t on different tasks of RoboCasa Kitchen nasiriany2024robocasa are highly related to the action chunk size. It can be observed that it is difficult and sub-optimal to empirically set a fixed value for various manipulation tasks.
• Figure 2: An overview of AAC. The proposed Adaptive Action Chunking (AAC) algorithm operates solely at inference-time, without any extra training or architectural changes. Specifically, we exploit the action entropy of continuous and discrete values as the cue to adaptively determine the optimal chunk size $h^*$ in each action chunk at the current observation. Therefore, we can achieve a favorable trade-off between consistency and reactivity in the entire episode, and substantially improve success rates across a variety of manipulation tasks.
• Figure 3: Rollout of chunk sizes from AAC. The derived chunk sizes align with human intuitions with respect to different semantic phases: a large chunk size is observed during the transportation stage, while a small chunk size appears at the critical manipulation stage.
• Figure 4: Distribution of chunk size decisions from AAC. We show the chunk size distribution of episodes on the first task of LIBERO-Spatial: "Pick up the black bowl next to the cookie box and place it on the plate". The heatmap indicates the frequency of different chunk sizes at different decision timesteps. The red curve shows the mean chunk size at different observation timesteps.
• Figure 5: Execution examples for real-world tasks using AAC. Videos of complete execution trajectories will be publicly available.