ICLR 2026

# Action-aware Dynamic Pruning for
Efficient Vision-Language-Action Manipulation

Xiaohuan Pei1,*, Yuxing Chen1,*, Siyu Xu1, Yunke Wang1, Yuheng Shi1, Chang Xu1
1University of Sydney
Paper Code arXiv

Motivation. Visual token redundancy varies significantly across robot manipulation stages. VLA-ADP exploits end-effector motion as a dynamic gating signal to identify and prune redundant tokens at each timestep, reducing computation without sacrificing task success.

Real-world ALOHA demonstrations — VLA-ADP applied to OpenVLA-OFT (1.5× speed)

Task 1: Put the white mug on the plate and put the chocolate on the plate
Task 2: Open the top drawer and put the bowl inside
Task 3: Pick up the tomato sauce and place it in the basket
Task 4: Pick up the black bowl next to the plate and place it on the rack
1.49×
Real-world latency speedup
76.9 ms → 51.8 ms
88.3%
Real-world success rate
up from 85.8% baseline
1.35×
LLM speedup on LIBERO
at 30–40% token keep ratio
≤0.9%
SR drop at 50–70%
keep ratio (LIBERO)

## Abstract

We propose Action-aware Dynamic Pruning (ADP), a training-free, plug-and-play method that adaptively prunes redundant visual tokens across manipulation stages by combining text-driven token relevance with an action-aware gating signal derived from end-effector motion.

## Method Overview

ADP Architecture. ADP maintains an observation window of past states and uses end-effector velocity/acceleration to produce a dynamic gating decision. The gate selects between sparse and dense token retention ratios, and text-driven cross-attention scores rank tokens by relevance before pruning.

Token Pruning. Spatially redundant background tokens (low attention score) are removed while task-relevant tokens are preserved, maintaining action prediction fidelity.

## Experimental Results

### Simulation Results (LIBERO Benchmark)

Comparison against OpenVLA, SparseVLM, FastVLM, and other VLA methods across four LIBERO task suites (Spatial, Object, Goal, Long). VLA-ADP achieves 94.4–99.0% SR with 1.13–1.35× LLM speedup.

LIBERO benchmark task suites used for simulation evaluation: Spatial, Object, Goal, and Long.

### Real-World Results (ALOHA Robot)

VLA-ADP improves SR from 85.8% to 88.3% while reducing latency by 33% (76.9 → 51.8 ms), achieving a 1.49× speedup on real hardware.

Real-world experimental setup: bimanual ALOHA robot performing tabletop manipulation tasks.

## Citation

    @article{pei2025action,
      title={Action-aware dynamic pruning for efficient
             vision-language-action manipulation},
      author={Pei, Xiaohuan and Chen, Yuxing and Xu, Siyu
              and Wang, Yunke and Shi, Yuheng and Xu, Chang},
      journal={arXiv preprint arXiv:2509.22093},
      year={2025}
    }

## Acknowledgements

We thank the authors of OpenVLA-OFT, OpenVLA, and Hugging Face Transformers for making their code publicly available. This project page was inspired by the Nerfies template.