Action-aware Dynamic Pruning for Efficient Vision-Language-Action Manipulation
Authors: Xiaohuan Pei
Yuxing Chen
Siyu Xu
Yunke Wang
Yuheng Shi
Chang Xu
Venue: ICLR 2026 Poster (2026)
Keywords: Vision-Language-Actions
Efficient Robotic Manipulations
Primary area: applications to robotics, autonomy, planning

Abstract: Robotic manipulation with Vision-Language-Action models requires efficient inference over long-horizon multi-modal context, where attention to dense visual tokens dominates computational cost. Existing methods optimize inference speed by reducing visual redundancy within VLA models, but they overlook the varying redundancy across robotic manipulation stages. We observe that the visual token redundancy is higher in coarse manipulation phase than in fine-grained operations, and is strongly correlated with the action dynamic. 
Motivated by this observation, we propose Action-aware Dynamic Pruning (ADP), a multi-modal pruning framework that integrates text-driven token selection with action-aware trajectory gating. ADP introduces a gating mechanism that conditions the pruning signal on recent action trajectories, using past motion windows to adaptively adjust token retention ratios in accordance with dynamics, thereby balancing computational efficiency and perceptual precision across different manipulation stages. 
Extensive experiments on the LIBERO suites and diverse real-world scenarios demonstrate that our method significantly reduces FLOPs and action inference latency (e.g. 1.35× speed up on OpenVLA-OFT) while maintaining competitive success rates compared to baselines, thereby providing a simple plug-in path to efficient robot policies that advances the efficiency and performance frontier of robotic manipulation.

Published as a conference paper at ICLR 2026
ACTION-AWARE DYNAMIC PRUNING FOR EFFICIENT MISION-LANGUAGE-ACTION MANIPULATION
Xiaohuan , Chen Yuheng Shi', Siyu Xu', Yunke Wang', Chang Xu Department of Computer Science, The University of Sydney, Australia xi xiaohuan, peeeepeiiii peipsyeapeeee (addedydde.... edu. au yche0009@uni. sydney edu. au (yuheng. shi, s.xu,
c. x.xu xuxu@syyneyy edu. au
ABSTRACT
Robotic manipulation with Vision- Leae--LLannage-cttiin models requires efficient in- ference over long-horizon multi-modal context, where attention to dense visual tokens dominates computational cost. Existing methods optimize inference speed by reducing visual redundancy within VLA models, but they overlook the varying redundancy across robotic manipulation stages. We observe that the visual token redundancy is higher in coarse manipulation phase than in fine- fine-graing grained operations, and is strongly correlated with the action dynamic. Motivated by this observa- tion, we propose Action-aware Dynamic Pruning (ADP), a multi-modal pruning framework that integrates text-driven token selection with action-aware trajectory gating, Our method introduces gating mechanism that conditions the pruning signal on recent action trajectories, using past motion windows adaptively adjust token retention ratios in accordance with dynamics, thereby balancing compu- tational efficiency and perceptual precision across different manipulation stages. Extensive experiments demonstrate that our method significantly reduces FLOPs and action inference latency (e.g. 1.35x speedup on Open VLA-OFT) while maintaining competitive success rates compared to baselines, thereby providing a simple plug-in in path to efficient robot policies that advances the efficiency and performance frontier of robotic manipulation. Project is available at ADP
the LIBERO suites and diverse real-world scenarios
INTRODUCTION
Large vision language models Liu et al. (223c b 2024a) Team al. (2033 | Awadalla et (2023)
have recently been extended into Vision-Language Action (VLA) models Kim et al. (2024 2025):
;
Black et al. 22044; Li et (2024): ; Brohan et al. (2024); Wen et al. 2205bb a); Bjockk et (2025)
that map both the visual observation and language instruction to executable robot actions. In the mainstream pipeline, vision encoder produces dense visual tokens from one or more camera views, projector aligns them to the language space, and an LLM fuses all modalities to predict actions. However, this multi-modal design introduces long input sequences with numerous visual tokens that only weakly relevant to the current manipulation operation, which inflates compute, memory footprint, and latency, and it can dilute attention over truly resk-relevant cues.
Existing work pursues efficiency via architectural lightening and modality- aware compression, such as RoboMamba Liu et al. (2024b) that focuses on lightweight designs, DeeR- VLA Yue et al. (2024) that aims structured pruningreparrmeeeeizziion
BLA Zhang al. (2025) that targets conditional layer activation, VLA-Cache Xu et al. 2025b) that focuses on cache reuse, and Efficient Yang al. 2025) that aims prune visual tokens via attention. However, key but underexplored property of robotic manipulation that visual redundancy in VLAs is action-aware across different manipulation stages. As Figure shows, during coarse-grained operations (e.g., relocating). global movement dominates and redundant tokens can be pruned; during fine- aneeggranedd phases (e.g., grasping), local geometry and detailed cues dominate and preserving full vision is preferred. Moreover, the relevance of visual patches is not only text conditioned (semantics of the instruction) but also action
"Equal contributions
1 "ooresponniig author.

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Published as a conference paper at ICLR 2026
Ours
0.0
Moving
Moving
Moving
60
(coarse)
(delicated)
(coarse)
(delicated)
(coorse)
O Observation
Observation
Observation Window
Observation Window
Observation Window Ps;
10:
Window
Window ps
0.5
0.0-
Static
Figure 1: Action-aware dynamic pruning vs. static pruning. We visualize five past observation windows as cases from a manipulation episode that condition the current anticipatory window, Pl Ps and Ps reflect coarse phases, prompting the gate to enable pruning to suppress redundant tokens, whereas P2 and L1 are delicate phases requiring detail vision context, so pruning is disabled and full vision is used. The curves depict robot's motion that drives the gating rule.
conditioned (instantaneous end-effector motion and gripper state). Treating all steps uniformly, or ranking tokens solely by mixed attention scores, therefore yields suboptimal pruning schedules that either prune too little (limited savings) or prune too much (accuracy loss), especially in multi-view settings (scene and wrist/gripper cameras) where importance unevenly distributed across time.
To address this challenge, we introduce Action-aware Dynamic Pruning (ADP), plug-and-play strategy that reduces computation while preserving manipulation fidelity. ADP is built on two com- plementary ideas: (1) Text-driven Pruming evaluates the relevance of visual patch using cross- modal similarities, selecting only the most relevant tokens before entering deep fusion in the subsequent layers. (2) Action-aware Dynamics modulate whether pruning is activated at a given step using a lightweight decision signal derived from the end-effector trajectory within each action window. Specifically, when the recent motion magnitude is relatively low compared to past motion statistics (delicate phases), pruning is disabled to preserve the full visual field for precise control. Conversely, when the motion magnitude relatively high compared past motion statisties (coarse phases), pruning is engaged to suppress redundancy and save FLOPs. We implement a gated mechanism that treats recent action statistics as a pruning signal over sliding trajectory windows, adaptively adjusting retention ratios according to motion dynamics and balancing efficiency with precision across manipu- lation stages. Our contributions can summarized as: (1) We show that the importance visual tokens in VLA models varies within different stages of robotic manipulation. This insight motivates our dynamic pruning method tailored to manipulation phases compared to static pruning approaches. (2) We propose text-driven action- pruning that combines task instruction relevance with gating rule based on end-effector motion, enabling adaptive switching between pruned and full-vision states. (3) We present principled complexity analysis and extensive experiments in both simulation and real-world settings, demonstrating that our method reduces FLOPs and latency while maintaining fine visual details required for successful manipulation.
2 PRELIMINARY
The mainstream Aision-language-attio paradigm extends large vision-language models to generate executable robot actions from multi-modal inputs-visual observations (scene and wrist/gripper views) and task instructions. A pre- pre-trained vision encoder produces visual tokens, projector aligns them to the LLM token space, and the LLM fuses modalities and autoregressively emits action tokens, which are de-tokenized into continuous 7-dimensional robot action.
Formally, given a sample of a scene image I* E IR. H W x3 gripper view 1 E xx and a task instruction I' Nx7. the vision encoder f (DINOv2 Oquab al. (2023) and SigLIP Zhai et al. (2023)) and text tokenizer fene project the multi-modal data into the same latent dimension space:
X
Scn (T", T9),
X f'n (T))
(1)
2

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Published as a conference paper at ICLR 2026
where X IR D and X
I max represent latent embeddings for vision and text.
VLA. In the latent space, the embedding representations are concatenated into a multi-modal sequence as:
X x [BOS] X ① X + X
where @ denotes concatenating embeddings of [BOS], vision, text, and proprioceptive (optionally) along the sequence length dimension, yielding multi-modal inputs T E IR 1 Lor+1, The
multi-modal sequence X is then fed into Large Language Model (LLM) fLLM (Llama2 Touvron jet al. (2023), which performs contextual reasoning and autoregressively generates an action token sequence:
p X
II fL X ررz))
(3)
where á = (a1....., E ? and T (1..... K} denotes the reserved action token space.
VLA-OF.. For optimize finetuning version, the paradigm shifts to parallel decoding without traditional autoregressive decoding stage. In the latent space, the method introduces Lact placeholder Xplace as inputs for actions positions:
m
[OS]
vis
+ X +
X txt
+ X
where X E tha
t Lua and the first placcholder as current action placeholder and another Lus actions future actions. During generation, the LLM directly predicts action tokens at action chunk in parallel:
À = fIIM(X"))) |I E T
(5)
where Á [aij... .ai | and each å; (āt, (.., ar,7) TT represents the tokenized 7-DoF action predicted at the i-th placeholder position. To expand action representation ability, the oft version also introduces the continuous control with token-level modeling. which each dimension of the continuous action is uniformly discretized into 256 bins. During generation, the 7 degrees of freedom action follows the standard gripper parameterization:
Ar, Ay. Az, ΔΦ, A0, Av, g]T
(6)
where (Az, Ay, Az) denote Cartesian displacements, (AØ, A6, A1)) denote Euler-angle rotations (roll, pitch, yaw), and g denotes the gripper.
3 RELATED WORK
Vision- LitionLLnggaae-Acttinn Vision-Language-Actins (VLA) models extend large vision-language models with an action generator that links multimodal perception to low-level robot control. They take images and text instructions, encode them into shared latent space, then decode it into executable actions. Early approaches Chi et al. (2023): Kim et al. (2024) map multimodal embeddings to discrete tokens, while more recent works Wen al. (2025b)) Kim al. (2025) emphasize continuous parallel decoding actions for improving performance. To this end, lightweight MLPs, diffusion- decoders, and parallel decoding strategies have been introduced transform hidden states into temporally consistent trajectories. Representative architectures include CogACT Li et al. (022)). Open Kim et al. (2024), OpenVLA-OFT Kim al. (2025), and म series Black et al. 2024).
which adopt diffusion modules for iterative refinement of continuous actions, as well as optimized fine-tuning frameworks that leverage placeholder tokens for parallel action prediction.
Efficient Robotic Manipulations. The high computational complexity of VLA models poses significant efficiency challenges for real-time robotic control. To address this, recent works have proposed efficiency-oriented strategies that can be broadly divided into training-aware and training- free approaches. Training-awan methods, such as RoboMambal Liu et al. (2024b) and DeeR-VLA Yue et al. 2024), redesign architectures or apply compression and pruning during training, achieving notable speedups while preserving accuracy. For instance, DeeR- VLA Yue et al. 2024) introduces dynamic reparameterization and structured pruning to reduce FLOPs, and Mole- VLA VLA et al.) (2025) selectively activates subsets of model layers conditioned on task demands, yielding scalable deployment. On the other hand, training-free methods aim to accelerate inference without retraining.
3

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Published as a conference paper at ICLR 2026
Prune
VLA
Function Dynamic
VLA
Observation Window Size
af Ot-w++ w a3 3-
dr--
Az
(a.)
(b.)
Figure 2: Overview of our proposed Action-aware Dynamic Pruning (ADP) for Vision Languuge (a.) Action-ave awwree gating: the pruning function adaptively determines whether to prune based on recent end-effector trajectories (Ar, Ay, Az, Aø, A6, Avh g) enabling dynamic pruning- (b.) Anticipatory pruning: task-relevant visual tokens are selected via attention-based relevance, while redundant patches are discarded before entering the VLA backbone.
Action models.
VLA-Cache Xu et al. (2025b) reuses keys and values of uninformative tokens between consecutive steps save computation, while EfficientVLA Yang et al. (2025) retain task-relevant patches identified via attention maps. However, these methods typically rely on single-layer heuristics or static rules that may overlook stage-dependent redundancy in manipulation tasks.
METHODOLOGY
In this section, we introduce our proposed method Action- aware Dynamic Pruning (ADP) VLAs for robotic manipulations. We first introduce text-driven pruning that identifies text-relevant tokens in Section 4.1 and then introduce action-aware dynamics that dynamically modulates the pruning strategy according to the past observed actions in Section 4.2
4.1 TEXT-DRIVEN ANTICIPATORY PRUNING
Before entering the LLM, the multimodal sequence X still contains large number of redundant visual tokens, which increase computation and dilute attention focus. To mitigate this redundancy, we compute the relevance of visual tokens with respect to task instructions at each layer, which is shown in Figure Let the hidden state at layer / be H RSxD
(H = X in Eq, 2 We partition H into the vision and text subsets (!) R and H (1) L Applying the projection matrices, we obtain query and key representations:
( Q
KOO
H ((O)
reshaped into multi-head form Q similarity then computed as
E x and (( R N The scaled dot- dot-product
11 (K)) E N
A
xL"
(8)
Vd
where each entry measures the degree to which a text token attends to a visual patch. To derive a global importance score per visual token, average across heads and text queries:
Nh
(v)
(9)
Nh.
List
b,t,
1=
Xeep Top-K (T((),, A 1p. 1
(10)
4

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Published as a conference paper at ICLR 2026
where (l...., Lvis), indexes visual tokens, Nohh is the number of attention heads, L is the text sequence length, and v is the total number of visual tokens. In multi-view scenarios with C' input
images (e.g., scene and wrist views), each contributing Le patches such that > L = L the retention quota is distributed across views by weighting vector RC with Σe 022- 1. The remaining visual tokens kept for view are
X vis (e)
Top-K K (8)) (e),, he = Lac k|,
(11)
where (1) (c) denotes the importance scores restricted to view For each view the remaining visual tokens can be represented as:
Prune o
bxt
ΣAAg
X
Ul
X
(o) ((g E
R
sys
vis
act
(12)
The reduced visual sequence Keep then concate- nated back with other modalities form the pruned multimodal sequence:
W-
W !
X X IBOSI vis keep
OXPOP
[HOS]
(13)
Step 1: Retrieval Weight
Step Prune
where X denotes the dynamically reduced input propagated to the LLM module.
sys
Figure Text-driven Anticipatory Pruning.
4.2 ACTION-AWARE DYNAMIC STRATEGY
Step 1: Retrieval pretrained weights from
Layer to compute relevance scores. Step Treat text guide to prune vision tokens based on the ranking.
While static pruning effectively identify task- relevant visual tokens, not every stage of manipu- lation task is suitable for relying solely on the pruned set. In particular, missing fine-grat fine-grained visual details may cause failures in operations such as moving, swiping, or aligning objects. The accumulation of such local errors can easily propagate and ulti- mately cause the entire task to fail. To address this limitation, we introduce a dynamic visual strategy that adapts the pruning decision to the robot's motion state across different task phases, guided by end-effector (EEF) trajectory within each action chunk, which focuses capturing both translational displacement and rotational motion.
Windowed trajectory and actions. Assume i indexes windows and indexes steps within window; b; and e, denote the start and end timesteps, and w matches the OFT action placeholder length. We treat each decoded action chunk temporal window [bi, ei] of length w e; b. with actions A ii a, E R where a. [Ari, Ayi.s Azi,, A A0; Awi.ss Fi.u
collects per-step translational and rotational increments and the gripper command.
Definition 4.1 (Windowed FK for EEF Position). Ler T₁ E SE(3) be the EEF pose at time and π : (3) 3 extract translation. With body-frame (right-muliply) composition,
Pbyta
To.
II
Ri
0
Vi. 1
)
Te.tu
To To
Rog 0
Viςu
(14)
where Vi,u
|ATiu: Ayj.u: A Aiju and Ri, = R. (Apr, (A0i..)) R. (AWi...))
The rotation order Rg Ry,RR follows our implementation: if world-frame (left-multiply) update or a different Euler order is used, the composition should be adjusted accordingly. Definition therefore makes P. = π (Te) an explicit function of the full 7-DoF action sequence within each window.
Windowed trajectory distance. Given p. from Definition 4.1 quantify motion per window either by the Fuclidean displacement:
8,
Pr41 Pella-
(15)
5

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Published as a conference paper at ICLR 2026
The windowed trajectory distance aims to yield a scalar ò; that captures overall motion magnitude and subsequently drives our pruning decision rule.
Dynamic decision function. Given windowed trajectory distance, define binary state variable 8t E {0, where s; = corresponds to the f full-vision state (w/o pruning) and s; = 1 to the prumed state (cross-attention pruning). Therefore, the next state can be determined by
1, 6, 8,
; 65.
81+ = f(6,) = 1. 5
8, =
(16)
8,:
The dynamic switch to S1+1 adapts to the global motion scale of the task, enabling pruning during periods of high activity while retaining full vision during fine- ine-grained phases. An alternative is the adjacent-extrema function, which sets thresholds based on the extrema of the most recent T windows:
U (+) max 6 A--++1
8.).
(17)
V(( min (6, -+1 5.3
with the update rule
1,
6, 2 U(1,
0,
6, < V(j
(18)
V(O) < < U())
8/s
This design aims to respond quickly to local motion variations, capturing abrupt efficient shifts between coarse and delicate manipulations. When the robot exhibits turge-amplitue motions, pruning is activated to suppress redundant visual input and reduce computation. When motion amplitude decreases, signaling the onset of fine manipulation, pruning is disabled to preserve the complete visual context necessary for accurate control.
4.3 THFORETICAL ANALYSIS OF COMPUTATIONAL COMPLEXITY
We consider the computational cost of the Transformer stack in fLLM with respect to the sequence length before and after pruning. Let S denote the token length of the full multi-modal sequence in Eq. 2 D the hidden dimension, and M the intermediate dimension of the feed-forward network (SwiGLU). The total number of vision tokens before pruning is Lvis while k -p- L,is] tokens kept after the text-driven selection in Eq. 10 We denote by T the number of forwards in a complete task execution (Eq. 5). and by 7 [0, 1] the proportion of forwards executed in the pruned state (Section 4.2 Let H be the number of Transformer layers in fLLM
For one Transformer layer, the FLOOPs approximated by
F(S; D, M) 25 D + 8SD2 6SDM,
(19)
corresponding to attention, projections, and MLP. A baseline forward of the H Hllayer LLM therefore costs
Fause
H F(S; D.
(20)
In ADP, pruning happens before the LLM the embedding stage: visual tokens are ranked and reduced using Eqs. 10 yielding the shorter sequence
S' = 1 k + Lpop + Lust + Lact + 1.
(21)
The scoring overhead uses lightweight projections and similarity matrix between text and vision embeddings,
Fsuere = 2T + 2T D2 2 2N " Trigd..
(22)
Assume pruning and scoring are performed once per forward before any Transformer layer, so all H layers operate on S', the lay-r cost follows F(S: D, M) 2S D 8SD2 + 6SDM, and D dd
A FADP = Prase - FADP, FADP Fscore + H F(S:; D,,
(23)
where A: = Lo LvisJ S' is as above with Lppop. Luxt Lnet the proprioceppion/eextaaiii lengths, N is the number of attention heads of size d, and Phose = H F(S; D, M). Over an episode of forwards, the expected complexity under the dynamic strategy is
E E [episodde]
T(> FADP + (1 7) Frase),
(24)

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Published as a conference paper at ICLR 2026
Table 1: Results the LIBERO Benchmark. TR: Training- FFee; AR: Auto-Regressive; PD: Parallel Decoding. Ratio: The retain tokens / Full tokens.
Method
TR
Decoding
CKPT
Spatial
Object
Goal
Long
Average
FL.PP
Specdupt
OpenVIA A Kim ot
20724
AR
OpenVLA (7B)
84.7%
88 4%
79.25%
53.7%
76.5%
SparsVLLM Zhiax
2124
OpenVLA (7B)
79.8%
67.0%
72.6%%
39.4%
64.7%
Chen
OpenVLAA (7B)
OpenVLA (7B)
83.8%
85 8%
76.45%
52,858
74.7%
FlasVVAA al
2014
AR
OpenVLA (7B)
$4.2%
86.4%
75.4%
51.4%
74.4%
SP-VLALL ar
OpenVLA (7B)
75.4%
85.6%
84.4%
54.2%
74.9%
WorldVEx
Chamelcon
WorldVLA Loree
Chameleon (7B)
87.6%
96.24%
8.3.4%
60.ORS
81.8%
NORA|Hung al 20/3
AR
Owen VL (3B)
85.8%
$7.85
77.0%
45.0%
73.9%
SmelV
Salai 2025
SmolVLM (2,25B)
93.0%
94.0%%
91.0%
77.07%
88.8%
CogVLM (78)
97.27%
NORA-I amp Fipir
Qwen-VL. (3R)
92.2%
95.
89.458
N7.
2025
FD
(FB)
98.65%
98. 2%
96.675
9/1,81
97.1
7.91
1,0KK
FosV(=OFT Chen
112
(7B)
96.8%
81 51.
96.4% 47%
73.0%%
86.879
6.37
1,24K
VLA-ADP (Ratioo30)%
VLA-ADP (Radioo4015)
OFT (78)
98.25%
977 255
6.99
87.25%
6.14
VLA-ADP (Ratio-00%)
OFT (78)
99.4%
98. (1%
96. 96.45%
91 01.
96.3%
6.43
1.28%
YLA-ADP (Ratio-69%)
PD
OFT (7B)
98.8%
98,
95.8%
92.0%
96.2%
6.74
1.17x
VLA-ADP (katio=70%)
Pick the bleck bow next plate and place it pleto
Pick The momo SaLICO and piace beckre
al
Open the drawer and put the bonl insido
PuE BID while mag the plata put Bie chocolass puddligg Do. Dal
FOLTE Dh plATe
Ap.
Figure 4: Visualisation of our method on representative examples of the four LIBERO task types (Spatial, Object, Goal, Long). Blue masks indicate pruned vision tokens. The retained tokens con- sistently highlight task-relevant objects, validating the Text-driven Anticipatory Pruning. Moreover, full vision tokens are restored at critical phases (e.g.. initialisation, grasping, placement), demonstrat- ing the effectiveness of the Task-driven Pruning and Action-aware Dynamic Strategy.
with expected savings
E [[AFFpisode = T'y AADPP
(25)
The pruning is applied at the embedding stage prior fLIM, so the reduced length S' benefits all H layers uniformly, and the dynamic rule (Eq, 16) controls how often the pruned path is used across action windows.
EXPERIMENTS
We conduct experiments across LIBERO simulation and real-robot tasks under standardized set- tings, comparing against strong baselines and conducting targeted ablations assess success rates, compute/latency, and the contributions of dynamic scheduling and layer-wise pruning.
SIMULATION EXPERIMENTS
Experiments setup. For simulation, we use LIBERO Liu al. (2023a) with four suites (i.e., Spatial, Object, Goal, Long) evaluating spatial understanding, object recognition, goal-directed behaviour, and long-horizon planning. All LIBERO settings follow VLe VFA-OFT, using its public run scripts. For comparison, we reproduce FastV Chen et al. (2024) Open environment. Experiments run on Linux with an NVIDIA RTX 4090. We set the window size to the OpenVLA-OPF chunk size (8) and apply cold start: the first two windows use full vision. To limit error accumulation, if pruning occurs three consecutive windows, the next window is forced to full vision. In multi-view pruning, the main wrist retention is 4:6. Within the action-awane Dynamic Strategy, we use Euclidean displacement and the adjacent-extrema rule; in its third case we deterministically set the state to (instead of inheriting s;) to further reduce FLOPS.
and run under the same

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Published as a conference paper at ICLR 2026
Table 2:
Real-World Experiments tasks), Task Task 4 cover the picking, placing and wiping motion, which covering variety of objects.
Method
Decoding
Task
Task2
Task3
Task4
Average
Latency,
Speedupt
OpenVLA-OFF (base)
PD
83.3%
93.3% 90.0%
86.7%
80.0% 83.3 %
85.8% 88.3 %
76.9 51.8
1.00
VLA-ADP (ours)
PD
90.0% %
90.0%
x.49x
and place into the green
"Ploce the carrot on the bike par"
"Place the thee cabe
Wiee
Figure 5: Real world experiments. We conduct the experiments on Jaco2 Real-world Platform.
Main result. Table shows that VLA-ADP achieves stable accuracy-comput trade-off across the keep ratio on LIBERO. Compared to VLenVLA-OO VLA-OFT, when the keep ratio 50-70%, the average success rate slightly decreases (I 0.9%). while the LLM-side inference speed improves by up to 1.23x, and the FLOPs are markedly below the baseline. Further compressing the retention rate to 30-40% maintains average SR of 94.4-94.8% yet attains 1.29-1.355 speedup. Notably, VLA-ADP achieves a 99.4% success rate on the Spatial, indicating that VLA-ADP successfully prunes redundant vision tokens while selectively preserving key information in relatively simple spatial manipulation scenarios. By contrast, Random Dropping (50%) yields a 1.29x speedup on the LLM side and performs reasonably on the Spatial and Goal, but its success rates on Object and Long are only 73.0% and 76.2%, respectively.
5.2 REAL-WORLD EXPERIMENTS
Experiment setup. We evaluate on four real tasks executed on a physical robot: Taskl: pick up the orange pot and place it in the green plate; Task2: place the blue cube the plate; Task3: place the carrot on the blue pan; Task4: wipe the table. All runtime settings mirror the simulation: Linux workstation with an NVIDIA RTX 4090, parallel decoding (PD) and window size equal VLe- OFT chunk size (8) with a two-window cold start (full vision). To curb error accumulation, if pruning enabled for three consecutive windows, the next window uses full vision. Within the Action-aware Dynamic Strategy we use Euclidean displacement and the cejjacent-extreme rule, and in the third case deterministically set the state to further reduce FLOPs.
Main result. Table 2 summarizes the outcomes. VLA-ADP improves the average success rate from 85.5 35.8% to 88.3% while reducing latency from 76.9 51.8 (baseline/ours 1.49x). Per task, VLA-ADP outperforms the baseline on T/s/1/3/4 (90.0%, 90.0%, 83.3%) and is still competitive on Task2 (90.0% vs. 93.3%). These results indicate that our dynamic pruning maintains or improves real-world success while yielding substantial speedup.
6 ABLATION STUDY
We conduct ablations each component of our method: first removing the dynamic strategy to test ADP (w/ Action-awar Dynamic); then ablating the pruning module (wo feivee Pruning) with different weight selections for relevance scoring.
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Table 3: Ablation Study on each component of our design. (a) Ablation on dynamic strategies (f). (b) Ablation on pruning based on weights retrieved from layer I. Metrics are SR (%) and FLOPs.
Sparial
Object
Goal
Long
Avg
Spatial
Object SR
Long
Avg
f
SR1
SR1
SR1
SR1
SR1
p"12
F1.0Ps]
SR1
SR1
SRT
SR 7
FLOPss
ADP
99.4%
98.0%
96.4%
91.2%
96.3%
0.22
6.43
0
99.4%
98.0%
96.4%
91.2%
96.3%
6.43
wlo D
98.2%
88.0%
96.4%
91, 2%
93.45%
0.25
6.23
1
98, 98.
97.6%
96. 2%
89.8%
95. 5%
6,57
PS
95.0%
81.4%
96.2%
87.0%
89.9%
4.55
98.0%
97.8%
91.2%%
95.8%
(a)
(b)
(a) Original Attention Maps
(b) Pruning Attention Maps.
(c) Vision tokens importance score.
Figure 6: The figure illustrates the layer-wise importance score of the vision tokens. (a) Vision-vision similariyy (across layers) (b) Text vision attention (after ADP) (c) Vision-token importance +
Action- -waree Dynamic Strategy. We assess the impact of the dynamic strategy with two baselines: removing the dynamic function (ADP w/o D) and a handcrafted periodic switching variant (w/o D + PS). Table Bal shows that introducing the dynamic controller (ADP) yields the best overall accuracy-comput balance: 96.3% average SR with pang = 0.22 and 6.43 FLOPs, outperforming the variant w/o 1) (93.45% pang = 0.25, 6.23 FLOPs) and the periodic schedule w/o 1) + PS (89. (89.9% p009 = 0.50, 4.55 FLOPs). Per-suite, the dynamic policy preserves Goal/Long while boosting Spatial/OOject- notably +16.6 point gain on Object over the periodic schedule (98.0% vs. 81.4%), and +4.4 on Spatial (99,4% vs. 95.0%). Compared to ADP w/o D, ADP improves average SR by +2.85 points with comparable compute (+0.20 FLOPs), indicating that state-awar switching (rather than fixed cycling) is crucial to avoid over- ever-pruning during fine manipulation while still pruning aggressively when large motions occur.
Impact of Pruning Strategy. We ablate layer selection to validate using layer 0 for importance scoring. Table 35 shows that final SR similar across layers, with layer yielding the best accuracy- compute balance: 96.3% average SR with 6.43 FLOPs, compared to layer I (95.5% 6.57 FLOPs) and layer (95.8%, 6.89 FLOPs). Meanwhile, FLOPs increase with deeper layers, so selecting I = 0 offers a superior performance-efffccenne trade-off. The slight decline in average SR with depth corroborates our analysis: deeper layers induce more localised attention-sharaeee sharpeninn peaks vs. non- peaks but reducing global state coverage thus becoming more sensitive occasional mismatches and noise, which leads to marginally lower success rates.
Observation of Attention Weights. A common view holds that text-vision alignment concentrates in deeper multimodal layers, so high importance scores should be computed there; however, this need not hold for parallel-decode
VLA. Our visualizations and measurements show that layer already provides a stable, discriminative text-to-vision signal. In Figure 6a] the layer-O self-similarity matrix shows clear high-contrast block structure, while deeper layers become increasingly diagonal-banded, compressing non-local correlations. The text to vision submatrix (Figure (b) follows the same trend: layer 0 exhibits pronounced peaks and troughs across many vision tokens, whereas deeper layers retain only a few narrow high-response bands. The token importance score & (Figure (6c) likewise has higher SNR at layer with depth, curves sharpen and develop long tails or near collapse, making top-k ranking more sensitive to local noise.
CONCLUSION
We presented VLA-ADP, a plug-and and-play pruning framework that unifies text-driven anticipatory pruning with action-aware dynamic strategy to accelerate VIA inference while preserving reliabil-
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ity. Across simulation and real-world evaluations, the method maintains or improves task success. reduces compute, and shortens inference latency. The dynamic controller consistently outperforms removal and periodic switching. and early-layer scoring offers the best accuracy- securacy-efficiency balance. These results indicate that adaptively pruning vision tokens by motion state and instruction relevance enables efficient, fine-grained manipulation without compromising control quality.
ACKNOWLEDGEMENTS
This work was supported in part by the Australian Research Council under Projects DP240101848 and FT230100549.
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APPENDIX
THE USE OF LARGE LANGUAGE MODELS
In this manuseript, used LLM as writing assistant to refine the presentation of certain contents.
Specifically, the LLM was used to: (1) Rephrase sentences and paragraphs for greater readability, conciseness, and academic formality. (2) Correct grammar, spelling, and punctuation errors. (3) Improve logical flow and transitions between sentences. All content enhanced by the LLM has been thoroughly reviewed by the authors ensure accuracy. The core idea, technical contributions, and results of the experiment are entirely the original contributions of the authors.
ALGORITHM
Here we provide the full pipeline our proposed action-aware dynamic pruning for Dision-language action models.
Algorithm 1 Action-aware Dynamic Pruning (ADP) for VLA Inference
Require: Multimodal embeddings x"==[x(BOs] X Xpop X X X[BOS]]
Q/K weights (-(0), Wk rretention ratio p. view weights RE with E. a,=1, gating rule f(-) (mean or adjacent-extremma) windowed actions A pose Tb.
Output: Per-window visual state E {0, 1}, pruned sequence X actions à
1: Init: 51 + (cold start, full vision), 82 0; consect-
> two-window cold start
2: for window = 1,2.... do
3:
Compute windowed motion 8, via FK on A (Def. 4.1
with p. π (Ti), Te1 8 ||P++1 - Prll2
Rg 0
Vi, 1
4:
Gate update: 8,+1 + f(ō.)
5: if consec 2 3 then
reset avoid prolonged pruning
6:
8i+1 + 0; consec t- 0
7: Branch by state
8:
if 8:41 =1 then
> pruned path
9:
Q/K scoring layer 0:
Q -H (h ( H (0) W(O) A 10) ( (K:(T))
Vi
N*Lm En, A (0) h
Aggregate importance (Eq. 9
11:
+ (p L' h2 + la h for c=..c
Per-view Top-k: X + Top-K( :e):: ke): X U
vis vis
(c)
(c)
(c)
Form pruned input: X [XBOS)] X X Xtx X x [FOS]]
14:
LLM forward: å (X"); consec consec +
15:
else
P full-vision path
16:
+ + fX consec +
17:
Execute å and advance to next window
18:
return {s:), Xm
REAL-WORLD EXPERIMENT DETAILS
We provide additional details of the real-robot experiments to facilitate reproducibility.
Robot Platform. All real-world evaluations were conducted Kinova Jaco2 6-DoF robotic arm equipped with a parallel-jaw gripper. The robot was controlled through Cartesian velocity commands 10 Hz, with joint and workspace safety limits enforced to prevent collisions.
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Sensing Setup. A single RGB camera (Sony AX53) was mounted in front of the table to capture the entire workspace. The camera streamed RGB frames at 640 480 resolution and 30 FPS. No wrist-mounted camera was used in the real-robot experiments, and all observations were obtained from this fixed view.
Runtime Environment. All policies were executed on Linux workstation running Ubuntu 20.04 with an NVIDIA RTX 4090 GPU. Inference employed parallel decoding with an OFT chunk size of 8. Latency was measured as mean per-step inference time over 100 runs.
Task Setup. We evaluated four tabletop manipulation tasks: a) Task 1: Pick up the orange pot and place it into a green plate. b) Task 2: Pick and place a blue cube onto the plate. e) Task Pick and place carrot into blue pan. d) Task 4: Wipe the table. Objects were placed randomly within a x 50 em workspace area at the beginning of each trial. For data collection, each task was executed using gamepad teleoperation interface, with the human operator directly controlling the Jaco2 arm. We collected approximately 100-150 trajectories per task, covering diverse initial states and object positions. The collected demonstrations were then used to build the training dataset, and we fine-tuned OLenVLA-OPF separately on each task before deploying the model for evaluation in the real-robot experiments.
Evaluation Protocol. Each task was repeated 30 trials under randomized object initializations. A trial was marked successful if the object was placed entirely within the target receptacle (for Tasks 1-3) or if more than 80% of the designated area was wiped (Task 4). At the end of each episode, the robot was reset neutral home pose. Safety termination was triggered if joint torque exceeded preset thresholds.
QUANTITATIVE STATISTICS
To complement the attention visualizations with comparable quantitative evidence, we compute scale-free statistics at each layer from the text-to-vision importance vector & {o))) We first
normalize + as Pt = /5/ Ey= Φj and then evaluate the Participation Ratio (PR) and the Entropy (H):
PR
H(P)
Σ pe log Pt-
(26)
2i=1
PR approximates the effective number of participating visual columns, while H. quantifies the dispersion and multi-modality of the distribution. Together, they characterize the spatial coverage of alignment signals and the stability of Top- K ranking.
Figure presents the variation of PR across layers. shallow layers, PR remains relatively high, indicating that a large number of key vision tokens receive balanced attention and contribute to broad spatial coverage. As depth increases, PR rapidly decreases and stabilizes, reflecting a progressive concentration of attention onto small subset of tokens and reduced feature utilization.
Layonwise Support
Layonelse Entropy of Distribution
Panicuatin www.oeeeeeeee calmrr)
300
250
Entmas of momeattdd
50
20
20
20
30
Layar
Layer
PR across layers
(b) Entropy across layers
Figure 7: Layer-wise statistics of text-to-vision importance scores.
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Figure 7b presents the corresponding entropy H. across layers. Shallow layers exhibit higher entropy, suggesting diverse and smooth distributions that make Top-K selection robust against individual outliers. In contrast, deeper layers display sharply peaked distributions dominated by few tokens. While such localization may benefit semantic alignment in downstream reasoning, makes Top-K K selection unstable and prone to discarding critical tokens. These findings suggest that shallow-layer attention signals are more suitable for estimating the importance of vision tokens in VLA tasks. In particular, using layer-O offers computationally efficient choice, providing reliable importance estimates while further reducing FLOPs.
RANDOM PRUNING COMPARISON
Task: Pick up the black bowl the plate and place on the plate.
Task: Put both alphabet soup and the tomato sauce the basket.
Spatial Text-driven Anticipatory Pruning Long
Spatial
Random Pruning Long
Figure 8: ADP (Text-driven Pruning) vs. Random Pruning.
Random Pruning yields competitive performance on Spatial and Goal, but performs substantially worse Object and Long. We attribute this phenomenon the distribution of visual patches spanned by the key object as shown Figure g When the target object spans a large number of patches, random pruning still has a high probability of retaining the relevant patches, thereby preserving sufficient task-critical vision information. In contrast, random pruning is more likely to prune the relevant patches when the key object spans only small number of patches. Such pruning may result in incorrect object identification, which in turn propagates errors and ultimately causes task failure.
INVERSION EXPERIMENT
Table Inversion Experiment.
Keep Ratio=30% %
Keep Ratio=50%
Keep Ratio=70%
Task
Direct
Inverse
Gap
Direct
Inverse
Gap
Direct
Inverse
Gap
Spatial
97.6%
44.0%
53.6%
99.4%
54.8%
44.6%
99.0%
90.8%
8.2%
Object
98.4%
0.6%
97.8%
98.0%
11.8%
86.2%
98.2%
59.6%
38.6%
Goal 97.4%
43.6%
53.8%
96.4%
63.6%
32.8%
96.8%
84.8%
12.0%
Long 84.2%
0.2%
84.0%
91.2%
8.2%
83.0%
91.2%
37.4%
53.8%
Avg 94.4%
22.1%
72.3%
96.3%
34.6%
61.7%
96.3%
68.1%
28.1% %
To further evaluate the reliability our text-driven importance scores, we design an inverse-pruning experiment that uses the same experimental setup the main results. Instead of retaining the most relevant visual tokens identified by our text-vision relevance ranking, we intentionally preserve the least relevant ones, while keeping all other factors, including model, hyperpurameters, tasks, and ratios (30%. 50%, 70%), unchanged. the relevance ranking were noisy or poorly aligned with task semantics, the performance gap between direct and inverse pruning would be small. Conversely,
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large and systematic divergence would indicate that the scoring mechanism effectively captures task-relevant visual information.
The results in Table show clear and consistent performance gap between the settings across all suites and keep ratios. Under a 30% keep ratio, the average success rate drops sharply from 94.4% (direct) 22.1 1% (inverse), with near-complete failure Object (0.6%) and Long (0.2%) Similar trends persist at 50% (from 96.3% to 34.6%) and 70% (from 96.3% to 68. 68).%) Notably, inverse-70% still performs far worse than direct-30%, highlighting that semantic relevance dominates task performance over the number of retained tokens.
Notably, the degradation is particularly pronounced Object and Long, which require fine-grained spatial reasoning and long-horizon planning. Under inverse pruning, the model fails to preserve the high-ranked, lask-relevant visual tokens. This provides strong evidence that our importance scores do not drift toward irrelevant regions but consistently identify and prioritise semantically aligned visual tokens. In summary, these counterfactual results further support the conclusion that:
1. Task-relevant visual tokens play a decisive role in VLA tasks, and task success depends far more on preserving these semantically aligned tokens than on the number of tokens retained. Task performance degrades sharply as soon as these high-relevance tokens are removed, demonstrating that semantic alignment is the dominant factor influencing model performance.
2.
The text-driven relevance scoring mechanism effectively identifies the task-relevant visual tokens, showing that ADP's anticipatory pruning relies on a stable and meaningful semantic signal rather than incidental attention fluctuations. The scoring module effectively captures the alignment between the task instruction and the image observations, identifying the visual patches critical for object grounding, spatial reasoning, and contact-rich manipulation.
COMPUTATIONAL TRADE-OFF ANALYSIS
Table FLOPs and speedup with 0.3 ratio. ADP(I) denotes pruning based on the weights from the
i-th layer.
Item
baseline
ADP (0)
ADP (2)
ADP (4)
ADP (8)
ADP (16)
ADP (24)
Keep Ratio
0.3
0.3
0.3
0.3
Scoring Layer
0
8
16
24
Seq. Len.
603
245
245
245
245
245
245
Vis. Tokens (kept)
512
154
154
154
154
154
154
Scoring FLOPs (T)
0.00
0.02
0.51
1.01
1.99
3.97
5.95
LLM FLOPs (T)
7.91
3.19
3.19
3.19
3.19
3.19
3.19
Total FLOPs (T)
7.91
3.21
3.70
4.20
5.18
7.16
9.14
Expected FLOPs (T)
7.91
5.85
6.06
6.27
6.71
7.58
8.45
Speedup (Orig/Exp..)
1.00
1.35
1.31
1.26
1.18
1.04
0.94
Table 6: FLOPs and speedup with keep ratio 0.4.
Item
baseline
ADP (0)
ADP (2)
ADP (4)
ADP (8)
ADP (16)
ADP (24)
Keep Ratio
1.0
0.4
0.4
0.4
0.4
0.4
0.4
Scoring Layer
8
Soq. Len.
603
296
296
296
296
296
296
Vis. Tokens (kept)
512
205
205
205
205
205
205
Scoring FLOPs (T)
0.00
0.02
0.51
1.01
1.99
3.97
5,95
LLM FLOPs (T)
7.91
3.86
3.86
3.86
3.86
3.86
3.86
Total FLOPs (T)
7.91
3.88
4.37
4.86
5.85
7.83
9.80
Expected FLOPs (T)
7.91
6.14
6.35
6.57
7.00
7.87
8.74
Speedup (Orig/Exp.)
1.00
1.29
1.13
0.90
To quantify the computational trade-off introduced by anticipatory pruning, we evaluate the FLOPs associated with importance scoring and compare them against the savings obtained from reducing the
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Table 7: FLOPs and speedup with keep ratio 0.5.
Item
bascline
ADP (0)
ADP (2)
ADP (4)
ADP (8)
ADP (16)
ADP (24)
Keep Ratio
1.0
0.5
0.5
0.5
0,5
0.5
0.5
Scoring Layer
0
4
8
16
24
Scq. Len.
603
347
347
347
347
347
347
Vis. Tokens (kept)
512
256
256
256
256
256
256
Scoring FLOPs (T)
0,00
0.02
0.51
1.01
1.99
3.97
5.95
LLM FLOPs (T)
7.91
4.53
4.53
4.53
4.53
4.53
4.53
Total FLOPs (T)
7.91
4.55
5.04
5.53
6.52
8.50
10.47
Expected FLOPs (T)
7.91
6.43
6.64
6.89
7.30
8.17
9.04
Speedup (Orig/Exp.)
1.00
1.23
1.19
1.15
1.08
0.97
0.87
Table 8: FLOPs and speedup with 0.6 ratio.
Item
baseline
ADP (0)
ADP (2)
ADP (4)
ADP (8)
ADP (16)
ADP (24)
Keep Ratio
1.0
0.6
0.6
0.6
0.6
Scoring Layer
0
2
4
8
16
24
Seq. Len.
603
399
399
399
399
399
399
Vis. Tokens (kept)
512
308
308
308
308
308
308
Scoring FLOPs (T)
0.00
0.02
0.51
1.01
1.99
3.97
5,95
LLM FLOPs (T)
7.91
5.21
5.21
5.21
5.21
5.21
5.21
Total FLOPs (T)
7.91
5.23
5.72
6.22
7.20
9.18
11.16
Expected FLOPs (T)
7.91
6.74
7.16
7.60
8.47
9.34
Speedup (Orig/Exp.)
1.00
1.17
1.14
1.10
1.04
0.93
0.85
LLM sequence length. Tables 9 report detailed FLOPs measurements across range of keep ratios and scoring layers (all FLOPs reported in this section are computed under the original multimodal sequence length of 603.)
Across all practically meaningful configurations, the scoring cost remains substantially smaller than the compute saved by pruning. When importance ranking is performed using shallow layers (e.g. layers 0-4), the scoring overhead remains within 0.02-1.011 FLOPs. In contrast, shortening the visual sequence reduces the L.LM compute from 7.91T to approximately 3. 19-55.90T, yielding 2-4T reduction. The net effect is therefore dominated by the savings from operating on shorter sequence, and the overall inference
decreases consistently.
When combined with the action-aware dynamic gating strategy, the expected inference cost reflects the mixture of pruned and full-vision windows. In our implementation, the dynamic gate activates pruning for roughly 44% of windows FLOPs per forward are 5.85T (keep=0.3), 6.1 (keep=0.4), and 6.43T (keep=0.5), corresponding to 1.35x, 1.29x, and 1.23 speedup over the baseline. These values already account for the scoring overhead, confirming that the computational benefit persists under realistic usage.
average across LIBERO. Under this configuration, the expected
We also observe that performing relevance scoring at excessively deep layers (216) increases the overhead to 3.97-5.95T FLOPs, which approaches or surpasses the FLOPs saved by pruning. such cases, the net speedup degrades and may even become slightly negative. However, such configurations are not required in practice. Our ablation study already shows that layer-O scoring not only minimises computational overhead but also provides the most reliable and stable importance estimates, making the default choice in experiments.
Overall, the additional scoring computation is far smaller than the savings achieved through reduced sequence length for all practical settings, and the use of shallow-layer scoring ensures that the trade-off remains consistently positive. These results demonstrate that the proposed pruning strategy offers genuine computational benefits without compromising performance.
17

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Published as a conference paper at ICLR 2026
Table 9: FLOPs and speedup with keep ratio 0.7.
Item
bascline
ADP (0)
ADP (2)
ADP (4)
ADP (8)
ADP (16)
ADP (24)
Keep Ratio
1.0
0.7
0.7
0.7
0.7
0.7
0.7
Scoring Layer
0
4
8
16
24
Scq. Len.
603
450
450
450
450
450
450
Vis. Tokens (kept)
512
359
359
359
359
359
359
Scoring FLOPs (T)
0,00
0.02
0.51
1.01
1.99
3.97
5.95
LLM FLOPs (T)
7.91
5.88
5.88
5.88
5.88
5.88
5.88
Total FLOPs (T)
7.91
5.90
6.39
6.89
7.88
9.85
11.83
Expected FLOPs (T)
7.91
7.03
7.24
7.46
7.89
8.76
9.63
Speedup (Orig/Exp.)
1.00
1.13
1.09
1.06
1.00
0.90
0.82
CASE DISCUSSIONS
Base
O
(a.)
Ours
(b.)
Figure 9: Baseline Failed but Ours Success on #Episode 110 of Spatial task.
Performance Improvements through Pruning. We observe intriguing phenomenon: several scenarios, the pruned model performs even better than the original baseline. To better understand this behavior, we further examine representative cases and analyze the underlying factors that contribute to such improvements. We identify representative case that illustrates why moderate pruning can outperform the unpruned baseline. In Figure 94, the baseline model fails to grasp the bowl, whereas in Figure 99 ADP method succeeds with stable grasp. A closer inspection shows that the baseline allocates non-trivial attention to large, static background surfaces, effectively behaving like "sink" regions that dilute focus. After pruning these low-value background tokens, the model concentrates its attention on the immediate manipulation region opeiff-seecffically the bowl and its supporting surface. This reduction peripheral noise results in a more discriminative and task-aligned visual representation, helping the policy make more precise action decisions. Thus, in cases where background clutter dominates the unpruned attention distribution, pruning can improve operational focus and lead to higher success rates than the original baseline.
Limitations in Long-Horizen Tasks. In addition the individual examples, we analyze the per-task performance gap between ADP (keep ratio 0.5) and the Open baseline across all LIBERO suites (Tables TO 13). The performance drop is almost exclusively concentrated in the Long suite, while the Spatial, Object, and Goal suites remain largely unaffected, with comparable or slightly improved success rates. Notably, the largest degradations occur in tasks with long, compositional instructions involving multiple sequential subtasks, where the model must progressively shift visual attention across different stages and object sets.
This phenomenon highlights limitation of current VLA backbones. Since models such VLe OFT generate actions based only on the current multimodal context and static instruction, they lack explicit awareness of subgoal completion. Consequently, visual tokens associated with earlier subtasks may still be retained due to textual relevance, even after the subgoal has been achieved. Under constrained token budgets, this misallocation becomes amplified, leaving insufficient visual coverage for subsequent stages and leading to failure. Our inversion analysis further confirms that VL.A performance is highly sensitive to the availability of correct visual tokens.
18

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Published as a conference paper at ICLR 2026
Table 10: Spatial Suite Performance (ADP Bascline).
Task
Length
ADP
Baseline
Gap
pick up the black bowl between the plate and the ramekin and place it on the plate
82
100.0%
100.0%
0.0%
pick up the black bowl next to the ramekin and place it on the plate
68
100.0%
100.0%
0.0%
pick up the black bowl from table center and place it the plate
66
100.0%
100.0%
0.0%
pick up the black bowl on the cookie box and place it the plate
66
100.0%
98.0%
+2.0%
pick up the black bowl in the top drawer the wooden cabinet and place the plate
88
98.0%
94.0%
+4.0%
pick up the black bowl on the ramekin and place
63
98.0%
100.0%
-2.0%
on the plate
pick up the black bowl next to the cookie box
71
100.0%
100.0%
0.0%
and place on the plate
pick up the black bowl on the stove and place it
61
98.0%
98.0%
0.0%
on the plate
pick up the black bowl next to the plate and
66
100.0%
100.0%
0.0%
place it on the plate
pick up the black bowl on the wooden cabinet
70
100.0%
96.0%
+4.0%
and place on the plate
Table 11: Object Suite Performance (ADP vs Baseline).
Task
Length
ADP
Baseline
Gap
pick up the alphabet soup and place it in the basket
52
100.0%
98.0%
+2.0%
pick up the cream cheese and place the basket
51
100.0%
100.0%
0.0%
pick up the salad dressing and place in the basket
53
98.0%
98.0%
0.0%
pick up the bbq sauce and place it in the basket
48
98.0%
100.0%
-2.0%
pick up the ketchup and place it in the basket pick up the tomato sauce and place it in the basket
46
98.0%
100.0%
-2.0%
51
100.0%
100.0%
0.0%
pick the butter and place it in the basket
45
96.0%
98.0%
-2.0%
pick up the milk and place it in the basket pick up the chocolate pudding and place it in the basket
43
98.0%
96.0%
+2.0%
56
94.0%
92.0%
+2.0%
pick the orange juice and place it in the basket
51
98.0%
100.0%
-2.0%
While previous multimodal works Xu et al. (2025a): Wang et al. (0024): Su et al. 2025) have explored token pruning and efficiency trade-offs, these observations highlight that, in VLA settings, long-horizon compositional tasks introduce additional challenges related to subgoal progression and stage-aware attention reallocation. Future improvements may therefore require explicit mechanisms for subgoal estimation or dynamic visual attention redistribution.
IMPLEMENTATION DETAILS
Hyperparameters. We provide the full set of hyperparameters used our main experiments for reproducibility. These include all query-key (QK) pruning settings, dynamic gating parameters, and visualization options. Table summarizes the exact values used in all results reported in the main
text.
19

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Published as a conference paper at ICLR 2026
Table 12: Goal Suite Performance (ADP vs Baseline).
Task
Length
ADP
Baseline
Gap
open the middle drawer of the cabinet put the bowl on the stove put the wine bottle on top of the cabinet
37
100.0%
98.0%
+2.0%
25
94.0%
96.0%
-2.0%
41
94.0%
92.0%
+2.0%
open the top drawer and put the bowl inside put the bowl on top the cabinet push the plate the front of the stove put the cream cheese in the bowl turn on the stove
43
82.0%
88.0%
-6.0%
34
100.0%
100.0%
0.0%
40
98.0%
100.0%
-2.0%
32
98.0%
96.0%
+2.0%
17
100.0%
100.0%
0.0%
put the bowl on the plate
25
98.0%
98.0%
0.0%
put the wine bottle on the rack
31
100.0%
98.0%
+2.0%
Table 13: Long Suite Performance (ADP vs Baseline).
Task
Length
ADP
Baseline
Gap
put both the alphabet soup and the tomato sauce
61
94.0%
92.0%
+2.0%
the basket
put both the cream cheese box and the butter in the basket turn on the stove and put the moka pot on it put the black bowl in the bottom drawer of the cabinet and close it
58
96.0%
96.0%
0.0%
44
100.0%
98.0%
+2.0%
96.0%
94.0%
+2.0%
put the white mug on the left plate and put the yellow and white mug the right plate pick up the book and place it in the back compartment of the caddy
70.0%
100.0%
-30.0%
66
92.0%
100.0%
-8.0%
put the white mug on the plate and put the chocolate pudding to the right of the plate put both the alphabet soup and the cream cheese box the basket
86
80.0%
98.0%
-8.0%
65
98.0%
98.0%
0.0%
put both moka pots the stove put the yellow and white mug the microwave and close it
31
92.0%
80.0%
+12.0%
94.0%
92.0%
+2.0%
Code Availability. Our code and complete experimental configuration are publicly available at the project pagel The implementation exposes all gating and pruning parameters through unified configuration interface, making easy to reproduce and extend the experiments (e.g., varying window sizes, pruning ratios, and similarity layers).
Table 14: Complete hyperparameter configuration used in all main experiments.
QK-related Param
Dynamic Param
Param
enabled
mre
use dyn.dddnnncc.mm www.vallleeeeeeeeeeeeeeee _etrrteg
true
Crials wer_task
50
ak gkyayer
decision_netiin
adjaceat overlay_pruned watpy_pored_______e
[0.0,255]
3k _Kokop_raati
0.5
adhacent_varisne
extrema
overlay prane_ayannne
120
gk_ keep_ep"
[..4.0.)
adjacent_eee rene window
dideo__ps
10
sandon_ kandon_keap_ratio
0.5
adjacent_lookagee
randon_etade state_prtbbbbbbbee
0.5
delta_method
net
randon_reed
42
Limit_consecutiin
jarit_max_ecnseeee ccnseeurivs_prruee
20


Reviews:
[{"review_id": "x8HPmVuhIf", "reviewer": "Reviewer_EgjS", "summary": "This paper proposes a plug-and-play framework to improve the efficiency of Vision-Language-Action (VLA) models for robotic manipulation by dynamically pruning redundant visual tokens. The key idea is to combine text-driven token selection with an action-aware gating mechanism conditioned on recent end-effector motion. Experiments on the LIBERO benchmark and real-robot setups show that ADP reduces FLOPs and inference latency by up to 1.35× (on OpenVLA-OFT) with minimal accuracy degradation.", "questions": "- Could the authors provide results or qualitative visualizations on cases where the gating rule failed (e.g., incorrect pruning during fine-grained manipulation)?\n\n- How sensitive is performance to the window length and retention ratio? An ablation over these parameters would clarify the generality of the dynamic policy.\n\n- The real-world experiments used a single fixed camera, whereas the simulation setup used multi-view pruning, including a wrist view. How does the action-aware gate perform without a wrist camera, whose motion dynamics would be identical to the end-effector trajectory? Does this change simplify or complicate the tuning of the gating threshold?\n\n- Could the **dynamic pruning controller** be made learnable (e.g., via reinforcement learning) instead of relying on hand-crafted thresholds?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Unlike prior pruning or compression methods (e.g., EfficientVLA, DeeR-VLA, VLA-Cache), ADP leverages **action-conditioned motion dynamics** to modulate token pruning during robotic manipulation. The insight that **visual redundancy varies with manipulation phase** (coarse vs. fine-grained motion) is both intuitive and unexplored, giving the paper a clear conceptual novelty.\n\n- The paper's primary strength is its core insight that visual redundancy in VLA models is action-aware and correlates with the manipulation phase. This is a novel observation that moves beyond static or text-only pruning strategies.\n\n- The method is validated with strong empirical results in simulation and the real world. In simulation, it provides a clear accuracy-to-speedup trade-off, achieving a 1.35x speedup with only a 2.7% drop in success rate (at 30% ratio). On a physical Jaco2 robot, it demonstrates a 1.49x reduction in latency.", "weaknesses": "- The action-aware gating mechanism, while effective, is built on a stack of heuristics. This includes the choice of motion metric ($\\delta_i$ as Euclidean displacement), the specific gating rule (\"adjacent-extrema\"), the fixed window size ($\\omega=8$), and hard-coded reset rules (e.g., a two-window \"cold start\" and a forced reset after three pruned windows). It is unclear how these settings would generalize to new tasks, robots with different dynamics, or different control frequencies without tuning.\n\n- The pruning strategy relies on similarity scores from Layer 0. This is counterintuitive, as deeper layers are typically assumed to capture more fused semantic meaning, although the paper provides a good justification in Figs. 6 and 7.\n\n- The experiments primarily rely on OpenVLA-OFT as the base model and omit results on diverse backbones (e.g., Pi0, GrooT, SmolVLA). Code availability or hyperparameter details are not specified beyond window size and ratios (Sec. 5.1), which hinders reproducibility.\n\n- The paper notes possible degradation during fine manipulation (Sec. 4.2) but does not quantify how the dynamic switch may misfire or cause visual underrepresentation in complex scenes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "idmcWZRnyU", "reviewer": "Reviewer_Uych", "summary": "This paper introduces ADP, which is a pruning method for VLA. ADP adopts text-driven pruning and action-aware gating,", "questions": "(1)\tFor the text-driven pruning, the visual tokens with low text attention score are discarded. However, during VLA’s training, no explicit constraints are applied on the text to image attention. How to make sure the correctness of the pruned area?\n\n(2)\tSince the method is claimed to be a plug-and-play component, results on ADP with other backbone are welcome. Currently, in table 1, it seems ADP is only combined with OFT.\n\n(3)\tIn some cases, the pruned policy performs better than the baselines. It seems interesting. Is there any discussions on this part?\n\n(4)\tThe design principal is kind of heuristic. Some rules (especially for the action-aware part) seem to be tailored for this pick-and-place. Other experiments are needed to show the proposed principal is universal.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "This paper aims to tackle one of the crucial problems in robotics, namely the efficiency of deployment.\n\nThe idea is intuitive and the method show good empirical results.", "weaknesses": "The design principal is kind of heuristic. Some rules (especially for the action-aware part) seem to be tailored for this pick-and-place. \n\nThe experiments are focused on the OFT model. Not sure whether this proposed method is compatible with other backbones.\n\nSee questions below", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wJtbyLj9xQ", "reviewer": "Reviewer_dNCJ", "summary": "This paper proposes **Action-aware Dynamic Pruning (ADP), a plug-and-play pruning framework for Vision-Language-Action (VLA) models. ADP combines text-driven anticipatory pruning with an **action-aware dynamic gating strategy (to determine when pruning should be applied based on recent motion dynamics). The approach is validated on the LIBERO benchmark and real-world robot tasks, demonstrating notable FLOPs reduction (up to 1.35× speedup) while maintaining or even improving task success rates.", "questions": "1. Have you tested how ADP behaves under sensor noise or inaccurate motion estimation?\n\n2. Could the gating rule be differentiable and trained end-to-end with reinforcement signals?\n\n3. How does ADP interact with temporal caching or diffusion-based decoders in future hybrid models?\n\n4. What happens when action magnitude and task difficulty are decoupled?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Novel problem formulation:\n- The paper identifies an underexplored property of robotic manipulation—action-aware visual redundancy—and systematically exploits it to design a dynamic pruning scheme. This insight bridges the gap between static pruning  and phase-dependent motion dynamics.\n\n2. Methodological clarity and rigor:\n- The paper provides clear mathematical formulations for both text-driven token importance and motion-based gating.\n- The windowed trajectory definition and dynamic decision function are well-motivated and interpretable.\n- The theoretical complexity analysis (Eq. 19–25) convincingly quantifies expected computational savings.\n\n3. Comprehensive evaluation:\n- The paper conducts thorough experiments on simulation and real robot setups.\n- Ablation studies isolate the contributions of each component.\n- Visualizations (Figures 4–8) effectively support the claimed interpretability of the pruning mechanism.\n\n4. Strong empirical results:\n- ADP consistently outperforms static baselines in both efficiency and accuracy trade-offs. Especially impressive real-world latency improvement (1.49×) with maintained success rate.", "weaknesses": "1. While the gating mechanism is intuitive, the paper lacks an analysis of stability or convergence under fluctuating motion magnitudes. How sensitive is the dynamic switching rule (Eq. 16–18) to noise or suboptimal motion planning?\n\n2. The gating rules (mean or extrema) are empirical; there is no adaptive or learned thresholding. A sensitivity analysis of hyperparameters would strengthen the claim of robustness.\n\n3.The baselines are comprehensive but mostly training-free ones. Including training-aware compression methods (e.g., DeeR-VLA, Mole-VLA) in a fair fine-tuned comparison would make the argument of “plug-and-play” stronger.\n\n4.The ablation study primarily examines whether dynamic control is included, but does not vary pruning ratios adaptively within tasks. Showing per-stage pruning ratio curves would illustrate dynamic behavior more explicitly.\n\n5. The real-robot evaluation setup is described in the appendix, but lacks release of calibration or dataset details. Code availability and parameter settings for dynamic thresholds are not stated.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Xbo7HWo2QY", "reviewer": "Reviewer_4B1i", "summary": "The paper addresses the inference efficiency problem of Vision-Language-Action (VLA) models in robotic manipulation and proposes a method called Action-Aware Dynamic Pruning (ADP). The core observation is that the redundancy of visual tokens is not constant during long-horizon manipulation tasks—it varies across different operation stages: redundancy is higher during coarse movements, while fine-grained grasping requires more detailed visual information. Based on this insight, the authors design two mechanisms to tackle the issue.", "questions": "1. In Figure 1, the word “pick” is mistakenly written as “picke.”\n\n2. Although the paper discusses existing VLA acceleration methods (such as attention token pruning and structured compression), I think the authors should more clearly explain how ADP differs from these approaches in terms of innovation and performance boundaries.\n\n3. I am also curious about the trade-off: since the paper introduces additional modules to learn how to prune tokens, at what level of token reduction does the time saved roughly offset the extra computation introduced by these modules?\n\n4. From my previous experience, using text to attend to visual features often results in attention being paid to irrelevant regions. Does your method address this issue?\n\n6. The authors propose to determine whether the current stage is “coarse” or “fine” based on recent changes in the end-effector’s trajectory. However, is this mechanism generalizable? Does it require manually set thresholds? And when the task type changes (e.g., when the motion involves mostly rotation rather than grasping), could this mechanism fail?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The method is clearly structured, and the writing makes it easy for readers to follow.\n\n2. The experimental evaluation is relatively comprehensive.", "weaknesses": "1. I still believe that the core contribution (i.e., gating + pruning) demonstrates only a moderate level of novelty since it is relatively intuitive rather than a fundamentally transformative architectural innovation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)