EfficientVLA: Training-Free Acceleration and Compression for Vision-Language-Action Models
Authors: Yantai Yang
Yuhao Wang
Zichen Wen
Luo Zhongwei
Chang Zou
Zhipeng Zhang
Chuan Wen
Linfeng Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Efficient AI
Embodied AI
Vision-Language-Action Model
Primary area: deep_learning
TL;DR: We propose a new paradigm to accelerate different components of the VLA model and achieved state-of-the-art performance.

Abstract: Vision-Language-Action (VLA) models, particularly diffusion-based architectures, demonstrate transformative potential for embodied intelligence but are severely hampered by high computational and memory demands stemming from extensive inherent and inference-time redundancies. While existing acceleration efforts often target isolated inefficiencies, such piecemeal solutions typically fail to holistically address the varied computational and memory bottlenecks across the entire VLA pipeline, thereby limiting practical deployability.  We introduce EfficientVLA, a structured and training-free inference acceleration framework that systematically eliminates these barriers by cohesively exploiting multifaceted redundancies. EfficientVLA synergistically integrates three targeted strategies: (1) pruning of functionally inconsequential layers from the language module, guided by an analysis of inter-layer redundancies; (2) optimizing the visual processing pathway through a task-aware strategy that selects a compact, diverse set of visual tokens, balancing task-criticality with informational coverage; and (3) alleviating temporal computational redundancy within the iterative diffusion-based action head by strategically caching and reusing key intermediate features.
We apply our method to a standard VLA model CogACT, yielding a $1.93\times$ inference speedup and reduces FLOPs to 28.9%, with only a 0.6% success rate drop in the SIMPLER benchmark.

EfficientVLA: Training-Free Acceleration and Compression for Vision-Language-Action Models
Yantai Yang! 1.2 Yuhao Wang 1. Zichen Wen' Luo Zhongwei' Chang Zou Zhipeng Zhang
Chuan Wen Linfeng Zhang
School of Artificial Intelligence, Shanghai Jiao Tong University
Harbin Institute of Technology
' an Jiaotong University
University of Electronic Science and Technology of China Anyverse Dynamics
O Code: http:////gttiuooooouuuauaaaaauuu
Abstract
Vision- Vision-Language-Actton (VL.A) models, particularly diffusion-based architectures, demonstrate transformative potential for embodied intelligence but are severely hampered by high computational and memory demands stemming from extensive inherent and inference-ting redundancies. While existing acceleration efforts often target isolated inefficiencies, such piecemeal solutions typically fail holistically address the varied computational and memory bottlenecks across the entire VLA pipeline, thereby limiting practical deployability. We introduce EfficientVLA, a structured and training-free inference acceleration framework that systemati- cally eliminates these barriers by cohesively exploiting multifaceted redundancies. EfficientVILA synergistically integrates three targeted strategies: (1) pruning of functionally inconsequential layers from the language module, guided by an anal- ysis of inter-layer redundancies; (2) optimizing the visual processing pathway through a task-aware strategy that selects a compact, diverse set of visual tokens, balancing task-criticality with informational coverage: and (3) alleviating temporal computational redundancy within the iterative diffusion-basse action head by strate- gically caching and reusing key intermediate features. We apply method to standard VLA model CogACT, yielding a 1.98x inference speedup and reduces FLOPs to 28.9%, with only 0.6% success rate drop in the SIMPLER benchmark.
Introduction
Building upon advances in multimodal understanding from models integrating vision and language [I] 2, 4 [], Lision-Language Action (VLA) models enable transformative embodied intelligence.
These systems, such as OLe (6], CogACT 7 To g and RT-2 J. directly translate multimodal inputs into executable actions, successfully tackling complex robotic manipulation and reasoning tasks using large-scale datasets (
10 Many cutting-edge VLAs couple Laingn-a Model (VLM) for scene and instruction parsing with diffusion model to handle multi-modal action distribution 1 [3]. However, the significant computational and memory overheads these Diffusion-bassed VLA architectures during the inference time pose critical barriers to their practical deployment, particularly for real-time interaction on resource-constrained robotic platforms.
Diffusion-basee based VLA architectures typically comprise a vision encoder to extract features, large language model (LLM) [14 [5
I7] core for multimodal reasoning, and diffusion biffusion-based action
"Correponning authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Table I: Module- inference characteristies of a baseline VLA model (CogACT, Left) compared to our proposed EfficientVLA (Right). EfficientVL.A demonstrates significant improvements overall inference speed and computational efficiency (FLOPs).
Visins Medale
I Languae Module
Action Medale
Vision Medule
Language Modilee
Action Module
#aam (M;
872.3
6738.9
59.0
Frrrm (M)
802.3
3971. (TTS))
59.0
Vision
Visson Token
(178%)
Denoising Steps
Denoising Steps
Inference Time (ms)
134.5
Inference
(149%)
FLOP% (CG)
3726.55
57.96
FLOPs
(178%)
(18028)
4.0
Time
FLD
0.24
3.5
0:23
PLOPs
3.0
Layer
Tayer
0.20
2.0
Computation Round
0.18
Kienory
Bound
Number of Visual Tokens
(a) Token-wise inference bottlenecks
(b) Layer-wise output cosine similarity (c) Timestep-wise output cosine similarity
Figure I:
VLA inference bottleneck and redundancy analysis: (a) Visual token pruning impact on
FLOPs and inference time, revealing computation- boundd and memory-bound regimes. (b) High inter-layer cosine similarity of LLM hidden states, indicating depth-wise redundancy. (c) Temporal cosine similarity of MLPPattenntion features in diffusion steps, showing computational redundancy.
decoder to predicts the final actions through multiple denoising steps. While this modular design underpins their powerful capabilities, it inherently results in substantial computational and memory overhead. Our findings (Table indicate that the language module and the iterative diffusion head are primary contributors to overall latency and computational load. Furthermore, as illustrated in Figure (a), while visual token pruning initially reduces inference time in computation-bout scenarios, its efficacy quickly diminishes as the system becomes meonory-bound by the LLM.
Prior VLA acceleration efforts have largely focused on isolated tweaks, delivering minimal overall gains. These fragmented approaches often fail because they ignore the integrated nature of VLA, where optimizing one module in isolation merely shifts bottlenecks. Gains are limited by unaddressed inefficiencies elsewhere, such as the memory demands of LLM or the computational intensity of action head. For example, methods like VLA [13] and DeeR-VLA [8] focus on specialized model architectures rather than broadly applicable inference acceleration frameworks for pre-trained VLAs. Other approaches, such Mole-VLA [19. tackle LLM layer redundancy but require costly retraining and overlook other pipeline stages. Similarly, VLA-Cache [20] caches static visual tokens but provides limited speedup, constrained by the significant memory footprint of LLM and computational demands of the action head. Consequently, these existing approaches fall short of providing truly holistic solution navigate the complex landscape of VLA inefficiencies.
To develop a more effective acceleration strategy, we systematically analyze the inference characteris- tics and multifaceted redundancies within each VLA module. In many Diffusion-based VLAs, the diffusion action head operates as a separate module, guided by features extracted from the VLM. This separation may underutilize the full reasoning capacity of VLM for action generation, questioning the necessity of its entire scale. As illustrated in Figure (bb, the language module demonstrates shows considerable depth-wise representational redundancy with high lnter-layer hidden state similarity. The visual processing pathway exacerbates this issue by processing superfluous tokens, characterized by low task-relevance or high informational overlap due to visual similarity, which strains computational resources and intensifies the memory-bound bound condition of L.M. As shown in Figure (c), the iterative diffusion action head displays significant temporal redundancy. The high similarity of its intermediate features across adjacent denoising steps implies extensive and staristatic recomputations.
Motivated by this, we introduce EfficientVL.A structured, training-fres acceleration framework for Diffusion-bar based VLAs that systematically targets these issues. Using a similarity-derive importance metric to target the primary memory bottleneck of the language module and its observed depth-wise

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redundancy (Figure[(bb) EfficientVLA employs selllarrtt--erivee importance metric to prune functionally inconsequential layers, thus reducing the depth of the model and the demands for memory without retraining. To manage the initial computational load from visual inputs before the memory of L.LM limit is reached (Figure (a)), our visual token pruning strategy tackles both task-relevant and inherent image redundancies by first selecting critical task-aligned tokens, then augmenting this set to ensure representational diversity while maintaining high task relevance. Lastly, EfficientVLA addresses temporal redundancy in the compute-intensiv action generator (highlighted by high feature similarity across timesteps, Figure (c)) by caching and reusing intermediate attention and MLP outputs, thus curtailing redundant computations. This synergistie, structured approach provides more holistic alleviation of GPU compute and memory bottlenecks than isolated optimizations.
The main contributions of this work are summarized as follows:
1. We present a systematic analysis identifying critical computational and memory-boumd bottlenecks, alongside multifaceted redundancies within contemporary Diffusion-baset Vision-Language Action (VLA) architectures, thereby motivating the need for structured acceleration.
2. We propose Efficcient a novel training-free, structured inference acceleration framework that synergistically prunes redundant layers from the language module based on their informational impact and strategically selects a compact, task-focused subset of visual tokens by considering both VLA task relevance and inherent image feature diversity.
3. Our framework further enhances efficiency by exploiting temporal redundancies the diffusion- based action head, introducing a caching mechanism for intermediate attention and MLP computa- tions during the iterative denoising process.
4. We demonstrate the efficacy of EfficientVLA through extensive experiments on the CogACT in the SIMPLER environment (21), achieving a 1.93 inference speedup and reducing FLOPs to 28.9%, all while incurring a minimal accuracy degradation of only 0.6%. This will facilitate the application of large-scale VLAs on the resource-constrained robotics platforms in the real world.
2 Related Work
Vision-Language-Actton (VLA) Models. Vision- Vision-Language-Attion models 6
18 22
23
24
25 26
27] extend Vision-Language Models (VLMs) 28 29 30 by incorporating action generation, bridging the gap between perception and action. These models enable machines to understand visual and textual inputs and generate corresponding actions for tasks (22 32) such as robotic manipulation
and object retrieval. VLA models typically use pretrained VLMs 20 to encode visual and linguistic data into a shared representation, from which actions are generated either as discrete tokens or continuous values. prominent recent trend within VLA the adoption of diffusion models for generating coherent continuous action sequences. This paradigm is exemplified by models such as CogACT 77. VLX (12), DiVLA (I]]. To 88, and Tiny VLA (I3). Many of these diffusion-basse VLAs employ a componentized design: the foundational VLM processes visual and linguistic inputs to produce condensed feature representation, which then conditions a distinct diffusion- based action module responsible for the iterative generation of precise action trajectories. This often involves the VLM output steering the denoising process within the specialized action decoder.
Efficient Vision- Lision-Languaae-Actton Models. The computational complexity of VLMs
33
34
35
36
poses significant challenges for their real-time deployment, particularly in applications such robotic control that require rapid decision-making. To address this issue, recent efforts to accelerate VI_A models have been primarily categorized into training- aware and training-free free methods. Training- awaare approaches, such as RoboMamba (37), EfficientVLMM (38), and DeeR-VLA (18), focus on optimizing model architectures or applying compression techniques followed by retraining, achieving significant speedups while maintaining performance. For instance, DeeR- VLA reduces computational costs by leveraging dynamic reparameterization and efficient pruning strategies, which enable more flexible and scalable model deployment. Similarly, For example, Mole- [19 reduces computational costs by dynamically activating only a subset of model layers based on task-specific needs. In contrast, training-fres free methods, such as VLAA-ache (20), enhance efficiency by reusing previously computed results for unchanged tokens between consecutive frames, which is particularly beneficial in scenarios with minimal variation in visual input.
3

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Language Module
80
30
Action Head
Tokenizer
Ax (r
Langtige
prasto near popol
Visual Token Filler
70 Robor Action
Layer
Layer
Layer
Layer
Layer
Layer
Vision Encoder
Olscraaiio
AAtimiae Manti
Taak
Divesity Set
Topsus
Step
Diserity
prodiction
Figure Overview of the EfficientVLA framework, our training-free structured approach to
accelerate Diffusion-based VLAs. It employs: (1) pruning of redundant language module layers; (2)
VLA task-aware visual token selection balaneing task relevance and informational diversity; and (3)
temporal caching of intermediate featuresin the diffusion action head.
3 Method
3.1 Preliminaries: Vision-Language-Actue Models
UsiionLLagguag-Acttinn (VLA) models represent a class of multimodal systems designed to bridge perception, language understanding, and robotic action. These models typically process image observations and natural language instructions through a sequence of specialized modules to generate executable action sequences. The initial stage of our basic VLA model employs Vision Module, comprising powerful pre-trained encoders DINOv2 (3) and SigLIP 40). to transform the raw visual input Oimg into set of rich feature embeddings Fv. These visual features Fv, along with tokenized language instructions, are then ingested by a language model backbone. This LLM performs multimodal fusion and contextual reasoning to derive task-oriented representation conditioning signal, EVL. which encapsulates the understanding of the scene and the instructed goal. Finally, Diffusion-baase based Action Head takes the cognition feature extracted from the output feature FVL as input and predicts the final action space of a gripper with 7 degrees of freedom (DoF).
3.2
Vision-Language Model Pruning
3.2.1
Layer Redundancy Analysis
The language module within VLA models, typically a multi-layer Transformer decoder, is critical for multimodal reasoning but often introduces substantial computational overhead. Each layer é in such transformer updates its input hidden state (6) 1 via residual transformation: (f+1) = ( + (() 8()). where f(-) is the layer-specific function with parameters (()), d is the hidden dimension, and S is the sequence length. Our empirical analysis, illustrated Figure ② (b), reveals significant depth-wise representational redundancy within this language module component. Specifically, we observe high cosine similarity between the input (e) and output x (f+1) states for numerous, particularly deeper layers. This indicates that the effective transformation (() 0(0) imparted by these layers is minimal, rendering them functionally less critical and prime candidates for pruning to enhance inference efficiency with negligible impact on task performance.
3.2.2 Importance-Drie Non-Contiguous Layer Pruning
To address the identified depth-wise redundancy within the language module of VLAA models, first rigorously quantify the functional importance of each layer. Our approach aims to identify layers that contribute minimally to the transformation of hidden state representations, rendering them candidates for pruning. We define the importance score (f) for given layer based the principle that layer effecting substantial change to its input is more critical than one whose output closely mirrors its
input. Specifically, 7(0) is quantified as one minus the average cosine similarity between its input and output hidden states across representative dataset D of VLA training samples and all L token

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positions within each sample:
7 =
Σ
1
2 و ز للا 1 1|2
(c+1)
D
T.
(2)
i=1
where x.. Fij
(f+1) Rd denote the input and output hidden state vectors, respectively, at position of sample for layer f. A high cosine similarity signifies minimal transformative effect by the layer function [(x(() ((0)), resulting in low importance score 7(() and indicating functional redundancy.
Based on these importance scores, we employ non-contiguous pruning strategy. For an LLM comprising N layers, the importance score I(O) is computed for every layer E (1....,N}, N).. These scores are then sorted ascending order, yielding an ordered list of layer indices Lranked = [e()) (2)...., such that 1 (f()) < I(fras) .... . S [((x))) Subsequently, the first n layers from , are selected for removal from the model.
this list, (4(1),
3.3
and Diversity-Driven Visual Token Pruning
Visual token streams processed by VLA models, despite their rich informational content, frequently exhibit significant redundancy, imposing substantial computational and memory overhead. This redundancy typically manifests in two primary forms: (i) tokens possessing low relevance to the specific VLA task objectives and (ii) tokens that are informationally duplicative due to inherent visual similarities within the input. To counteract these distinct forms of superfluity, we introduce novel, training-free free, VLA task-aware visual token pruning methodology. Our approach strategically distills compact yet maximally informative subset of visual tokens, Vpruned V of predetermined size Kj final (from an initial set of Ntotol token embeddings V = {er, U2,... ................................ UN | derived from the input image). This is achieved by first anchoring the selection with task-critical tokens identified via attention analysis, and subsequently augmenting this core set by judiciously balancing continued task relevance with the explicit promotion of feature diversity through similarity measures. The retained visual tokens for inference can be found in the supplementary material.
3.3.1 Quantifying Task Relevance
To guide visual token pruning, we quantify the task relevance for each initial visual token (from set of NNotai) by leveraging cross-attention scores from selected VLM layers. These scores capture the attention from towards Lat task-defining contextual embeddings (c.g., language instructions).
Let (AAA) denote the attention from visual token v; the jth contextual token in the 4 attention head (of H total heads). The raw task relevance score т; for U, is computed by first averaging attention contributions across all H heads for each visual-contextual pair (i, and then summing these averaged attentions over all Tatz contextual elements:
Lerz
H
H
These scores Tú, signifying each token's overall engagement with the task context, are sub- sequently normalized (e.g., via min-max scaling) to standardized scores E [0, 1] for robust comparison and subsequent token selection.
3.3.2 Selection of Key Task-Relevan Tokens
Armed with the normalized task relevance scores {s;}. the first phase of pruming identifies initial set of Keey visual tokens (e.g., Kkey empirically set between and 8) that demonstrate the highest relevance the VLA task. These tokens constitute the and indispensable visual token set, Vkey
Vkey {wz E V 8; is among the top K Kkey scores in {st 1kkk
Nioca!
(3)
The tokens in Vkey are unconditionally retained in Vpraned, forming foundational scaffold of visual cues deemed essential for task comprehension and successful execution. The set of remaining candidate tokens for further consideration is denoted as Vrem V Vkey.
5

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3.3.3 Augmentative Selection Balancing Relevance and Diversity
To supplement the core set Vkey and achieve the target final token count K final, additional Kaug = K final Kkey tokens are meticulously selected from Vrem- This crucial augmentation phase is guided by ratio E [o, 1], which orchestrates a hybrid selection strategy that concurrently promotes continued emphasis on task relevance and the introduction of informational diversity.
Yask-Driven Augmentation. A fraction of the augmentation quota, specifically Ktask = [ K aug. tokens, is selected from Vrem by further prioritizing tokens based on their high task relevance scores 8;. Viast reinforces the task-centric nature of the pruned representation by incorporating additional tokens that, while not part of the initial Keey elite, still exhibit strong relevance signals. These tokens are added to the selection, and the pool of remaining candidates is updated: Vrem f Vrem Vinsk.
Diversity-Driven Augmentation. The remaining Kdio Kang Kass tokens are selected from the updated Vrem with the explicit objective of maximizing feature diversity relative to the key selected tokens. This step is vital for capturing broader spectrum of visual information and mitigating inherent redundancies not addressed by task relevance alone. For each candidate token Vj E Vrems its dissimilarity to the set Viey is computed. A common measure the cosine distance, ensuring that selected tokens are distinct in the embedding space:
Diversity Vkey) = 1 max Vj 0k E Viee ||5yle/|wall2
44)
The Kaie tokens from Vrem exhibiting the highest dissimilarity scores (i.e., those maximally different from already selected tokens) are chosen to form the set Váev- This targeted inclusion diverse tokens ensures the final selection is not overly specialized and retains richer contextual understanding.
Final Pruned Visual Token Set. The comprehensive set of visual tokens retained after pruning is
the union of these strategically selected components:
Vpruned Vee Veask Vaie
This final set V Vpraned. of cardinality K finat, is subsequently utilized for all downstream processing within the VLA model. This systematic reduction in visual sequence length significantly alleviates computational demands while preserving critical task-specific and diverse visual information.
3.4 Caching Intermediate Features in Action Prediction
Generating high-fidelity action sequences with Diffusion-based VLA models involves an iterative denoising process that demands significant computation due to repeated self-attention and MLP computations over T timesteps. We observe strong temporal coherence in the intermediate features produced during action generation (Figure (c)), indicating substantial redundancy across timesteps. To address this inefficiency and accelerate the action generation phase, we propose a static caching mechanism. This strategy periodically recomputes and caches critical intermediate attention and MIP output at a fixed interval N, reusing these cached values for the intervening time steps in the generation of action sequences. This selective computation aims to significantly reduce the computational cost associated with generating the action sequence while preserving its quality.
3.4.1 Feature Generation and Temporal Coherence in DiT Blocks
Let f denote the current denoising timestep, typically iterating from an initial Tstart down to 1. Within each DiT block at timestep the input features z, (which may incorporate cognitive features f, from upstream VLM modules and the current noise estimate) are processed sequentially by self-attention module and an MLP module to produce intermediate hidden states:
h Self-Aut(z:))
(6)
h mlp MLPP(h." !nth + 2:)
(7)
These features, hęth and hi are fundamental to the denoising capacity of the diffusion model. Our observation of their high temporal coherence-meaning mmmduulc Rs module for many and module mopes-totivatts their periodic caching and reuse.

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Table 2: Performance of EfficientVLA the CogACT versus the other baselines in the SIMPLER environment. Settings vary by retained LLM layers (L) and visual tokens (T). Random Dropping denotes method involving the random retention of 1 12 visual tokens.
SIMPLER
Method
Training free PickCan
MoveNeur Drawer DrawerApple Average
FLOPs.
Speedup1
Params (B)
CogACT
91.3%
85.0%5
71.8%
50.9%
74.8%
100,0%
7.63
Random Dropping
9
20.4%
53.51
0.0%%
20.9%
55.5%
1.20%
7.63
Fasty
81.456
69.88
74.1%
7.83
Matching
VLA--adbe
92.09%
83.35
70.55
$1.69
74.4%
90.15
1.38%
7.63
VLA (L=28, T=1
95.3%
83.35
5.87
E(BicientLA (L-28, T-56)
94.71%
R2.4%
69.85%
55.016
75.5%
32.9%
1
5,87
ELlicientVAA (1=22, T=112)
94.0%
75.0%
EtBcientVLA (L-22, T-56)
93.318
81.3%
68.21
53.8%
74.2%
28.9%
1.93x
4,86
CogACT
89.6%
80
46.6%
61.3%
100.05
1.00x
7.63
Random Dropping
4,0%
16.3%6
15.695
0.01%
8.91%
55,5%
1.20%
7.03
Variont
Fady
91.44%
78.666
27.6%%
50.6%%
62.1%%
1.19%
7.63
VLA-Cache
91.7%
79.39
32.5%
45.8%%
62.35
82.696
1.37x
7.63
Agurgation
EllicentVAAA--2 12)12)
94.8%
77.6%
28.45
51.9%
63.25
45.1%%
1.57x
5,87
EtficlntVLA (L-228 T-36)
94..0%8
77.2%6
27,69%
51.3%
62.616
32.9%
5,87
EfBicientVIAA (L-22. T=112)
93.9%
76.4%
27.3%
30.8%%
62.1%%
38.25%
1.76x
4,36
EMBcentVLA T-26)
93.2%
26.9%
61.216
4,86
3.4.2 Static N-Step Caching Implementation
We define cache interval N (1 N ststart). At the initial timestep 1 Tsarrt, the features
h Tetrr and hp are computed via Equations 6 and 7 and stored in persistent cache, denoted Catta and Cmdp- For any subsequent timestep caches are updated if and only if (mod N) 0 (assuming t and f aligns with desired multiples for caching, e.g., Tstart, Tatars - N, Ttaars - 2N..... Thus, for such recomputation timesteps:
For
T Tstart. these features are recomputed and the
Catta + Self-Attn(z:) Cmlpp MLP(Cattnn + 2t)
(8)
(9)
And the outputs for this step are haTh Cattn and h mulp = Cmilp all other timesteps, where (mod N) o O, the computationally intensive Self-Attn and MLP operations are entirely bypassed.
Instead, the required features are directly retrieved from the most recently populated cache:
alln h
Cottn
(when t (mod N) o 0)
(10)
h
mip Crip (when / (mod N) + 0)
(11)
This static caching schedule effectively prunes the execution of these core modules for N - out of every N timesteps post-initialization, leading to a substantial reduction floating-pont ppont operations and latency for the action generation component of the VLA. The choice of N allows for tunable trade-off between acceleration and the fidelity of the generated actions, as reusing features for longer intervals might introduce slight deviations if underlying representations were change rapidly.
4 Experiment
4.1 Experimental Settings
Simulation Implementation Details. To assess our VLA model, we utilize the SIMPLER environ- ment (11, a simulation-based benchmark for table-top manipulation. SIMPLER is designed to closely emulate real-world dynamics for robots such as the Google Robot and WidowX, demonstrating robust alignment between simulation and real-world performance. The VLA model in this setup takes 224x224 RGB image observations and natural language task instructions (c.g.. "Pick coke can") as input and outputs a sequence of actions in 7-DoF Cartesian space. The SIMPLER supports two evaluation configurations: Visual Matching, which prioritizes fidelity to real-world appearances, and Variant Aggregations, which incorporates diverse conditions such as altered lighting, backgrounds, and surface textures. For the Google robot, SIMPLER provides both two evaluation settings, each featuring the same four tasks: 1) Pick coke can; 2) Move near; 3) Open/close drawer; and 4) Open top drawer and place apple. Success rate used the evaluation metric.
Baselines. Our primary experimental validation of EfficientVILA is performed on the CogACT which integrates powerful vision encoders (DINOv2 39 and SigLIP [40). a Llama2-77 2-B 14
7

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Table 3: Scalability Analysis: We evaluate mean success rate and inference time in a simulation environment of visual matching across various model sizes. Our EfficientVL.A configuration maintains L= 22 and T 56.
SIMPLER
Model
Action-Params
Methods
Average
Inference time (s)
Total-Params (B)
CogACT
73.3%
0.2156
7.55
CogACT-Small
13M
Visual
FffficientVL.A
72.6%
0.1
4.78
CogACT
74.8%
0.2342
7.63
CogACT-Base
89M
Matching
EfficientVLA
74.2%
0.1213
4.86
CogACT 76.7%
0,2628
7.85
CogACT-Large
308M
EfficientVL.A
76.1%
0.1312
5.08
language module for multimodal reasoning, and a Diffusion Transformer (DiT) for generating action trajectories. We benchmark against relevant baseline methodologies. These include Random Dropping approach, where 112 visual tokens are retained uniformly random, to evaluate the benefits of our guided vision token pruning. We further compare with FastV 42] a notable approach focused on accelerating inference by pruning redundant visual tokens, and VLA-Cache [20], which leverages temporal analysis to cache static tokens across timesteps. The experimental results and model details for xo on the LIBERO 43 can be found in the Appendix.
Implementation Details. For EfficientVL.A in addition to layer pruning, we further compressed the model parameters by adopting the PruneNet 44 configuration for L.M compression. Specifically, we applied a sparsity of 25% to the MLP layers of all Transformer blocks. For visual token pruning, we started from the 2nd Transformer layer with a ratio a 50% and Kkey 4 for key task-critical tokens. Furthermore, the cache interval was set to 5. All experiments were conducted on NVIDIA A40 GPUs, and the inference time was measured as the average single-step inference duration. More details can be found in the supplementary material.
4.2 Results on Simulation Environment
Main Results on SIMPLER. details the performance of our structured, entirely training- free pruning method in the SIMPLER environment. Our approach consistently excels across configurations retaining 22/28 layers and 56/1 12 visual tokens. For instance, pruning 10 layers with 1 112 tokens surpasses both CogACT and VLA-cache success rate and inference speed. Remarkably, on the pick coke can task, pruning 36% of parameters paradoxically improved the success rate from 91.3% to 94.0%, highlighting significant parameter redundancy in the VIA model. Conversely, random token dropping to 112 tokens drastically reduces the average success rate to 20.9%, affirming the superiority of our guided selection strategy. Furthermore, a 22-layer, 56-token setup achieved a 71.1% reduction in FLOPs with merely 0.6% drop in average success rate, demonstrating exceptional efficiency, In comparision, approaches like FastV [2] (T = 56) show that solely optimizing visual tokens yields only 1.21 x speedup due to unaddressed memory bottlenecks, despite acceptable task performance.
Efficiency Analysis. As demonstrated in Table Figure 3 our proposed method significantly outperforms previous baselines, achieving a 71.1% % reduction in FLOPS and a 1.93x speedup in inference time. In stark contrast, VLA-cache, when applied to the CogACT model, only reduces FLOPPs by 19.9% and achieves mere 1.38 speedup. This disparity substantiates our prior anal- ysis: VLA-cache, functioning solcly as a cache for visual tokens between adjacent time steps, is inherently constrained by memory bounds, thereby limiting the efficacy of token-only accelcration. Consequently, the structured framework of our system offers distinct advantages, highlighting our method's superior capability in balancing computational efficiency with robust performance.
illustrates the scalability our proposed method across different sizes of the CogACT model. With the primary difference among the three models being the param- eter size of their action modules, the results reveal that our method's ss effectiveness becomes more pronounced with larger models. Specifically, on the CogACT-Large model, our approach achieves 2.0x inference speedup, while performance only marginally decreases from 76.7% to 76. 7%. This increased impact is because action modules in larger models, having more parameters, inherently
Scalability Evaluation.
8

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LiticicatVLA-22
1,93x
EiBiientV1AA22
121.1%
1,71x
167.1%
EiticientVLA-28
EtticieVVA-288
CogACT
CogACT
Inference Time (6)
PTOOPs
Efficiency analysis in simulation, comparing FLOPs and inference time of our Effi- cientVLA variants against the original model backbone. EfficientVLA-22 and ELfi denote configurations retaining 22 and 28 LLM layers, respectively.
Figure 3:
Table 4:
Performance impact of varying visual token reduction ratios (Left) and diffusion action head cache intervals (Right) as applied within our Efficient framework.
Token Ratio
77.8%
56
71.8%
72
62.5%
96
56.2%
112
100.0%
256
Cache Interval
2
4
Inference time (5)
Accuracy
FLOPs
0.1866
95.0%
0.1870
95. 35.3%
0.1889
95.0%
0,1956
0,2342
91.3%
Accuracy
FLOPs (T)
Inference time (9)
0.2342
4.190
0.2031
4.161
0.1987
4.155
0.1953
4.150
0.1909
4.144
Table 5:
Ablation study our EfficientVL.A, where 'Layer' denotes applying only the LLM layer pruning component, and 'MLP' refers to distinct strategy of compressing 25% of MLP weights within each layer.
Model Compression
Visual Token
Action
Success
Inference
Speedupt
Layer
MLP
Pruning
Cache
Rate
Time (s)
Ex0
91.3%
0.2342
1.00
Ex1
X
X
95.6%
0.1866
1,25 x
Ex2
X
X
X
/
93.7%
0.1909
1.23x
Ex3
85.7%
0.1604
1.46x
Ex4
92.3%
0.1638
1.43x
Ex5
93.3%
0.1387
1.69
Ex6
95.3%
0.1592
1.47
Ex7
93.3%
0.1213
1.93x
exhibit longer inference times, thus allowing our method to yield more significant accelerations. These findings also underscore the robustness our method across models various scales.
Impact of Token Reduction Ratio and Cache Interval. J our experiments on the pick coke can task reveal distinct impacts of component-specific optimizations. Aggressively pruning visual tokens, down to retaining only 22%, effectively lessens computational load for substantial, near-lossless acceleration. However, inference speed gains largely saturate beyond this point, with further token reduction yielding only marginal improvements, thereby revealing dominant system- level performance bottlenecks. Separately, for the diffusion-bard based action generator, we observed that increasing the cache reuse interval N for intermediate attention and MLP features progressively and significantly accelerates action trajectory generation.
4.3
Ablation Study
We conducted an ablation study on the components of our proposed framework, using the pick coke can task an illustrative example. As suggested by prior analysis (c.g., Figure (a), solely optimizing visual tokens for VLA inference tasks yields limited acceleration; retaining just 56 tokens resulted mere 1.23x speedup, although the success rate paradoxically rose from 91. 11.3% to 95.6%. This underscores the inherent limitations of token token-centric optimization methods, such as VLA-cache, and affirms that achieving substantial VLA inference acceleration viable for hardware deployment ne- cessitates a more model -entric strategy. In contrast, our model compression approuch-cconurreenty pruning layers and compressing MLP in the remaining layers-achin accrevea a 1.43x speedup. Critically, when all components were integrated, a 1.93 speedup was realized, and the overall task success rate still saw improvement of 2% points. These collective results highlight the imperative and significance of adopting a structured framework for effective VLA inference acceleration.

---

5 Conclusion
In this paper, we addressed the critical challenge of high computational and memory overheads that impede the practical deployment of powerful Diffusion-based AssionLanguageeActtion (VLA) models. We proposed EfficientVLA, novel training-free, structured framework to accelerate VLA models. Our framework enhances efficiency by synergistically pruning redundant layers of language module identified by their minimal impact on transforming hidden states and by strategically selecting a compact set of visual tokens that balances VLA task relevance with inherent feature diversity. Furthermore, optimizes the action module by caching critical intermediate computations across its iterative denoising steps. We demonstrate the efficacy of Efficient through extensive experiments on the CogACT in the SIMPLER environment [21], achieving 1.93 inference speedup and reducing FLOPs to 28.9%, all while incurring minimal accuracy degradation of only 0.6%.
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A Experimental Settings
A.1 SIMPLER Environment
The SIMPLER simulation environment serves as our primary benchmark for evaluating CogACT models. It is specifically designed closely mirror real-world robotic setups, thereby facilitating more realistic evaluations and bridging the real-to-se sim control and visual gap.
SIMPLER offers two distinct evaluation settings:
This setting closely replicates real-world tasks by minimizing discrepancies between the simulated and real environments, prioritizing fidelity real-world appearances.
Visual Matching (VM):
Variant Aggregation (VA):
This setting builds upon Visual Matching by introducing varia- tions elements such background, lighting, distractors, and table texture, challenging models to generalize across diverse conditions.
For the Google robot setup, SIMPLER provides both evaluation settings, each featuring the same four tasks: 1) Pick coke can, 2) Move near, 3) Open/close drawer, and 4) Open top drawer and place apple. These four tasks for the Google robot are also illustrated in Figure
(a) Pick coke can
(b) Move near
(c) Open/ close drawer
(d) Open top drawer and place apple
Figure 4: Representative robotic manipulation tasks for the Google robot in the SIMPLER environ-
ment: (a) Pick coke can, (b) Move near, (c) Open/close drawer, and (d) Open top drawer and place apple.
A.2 Baselines
We benchmark our EfficientVLA against the following relevant baseline and backbone methodologies:
This model serves our primary experimental validation platform. CogACT integrates powerful vision encoders (DINOv2 and SigLIP) to process raw visual input, Llama2-7B language module for multimodal reasoning, and Diffusion Transformer (DīT) as a specialized action module for generating precise action trajectories. aims to synergize "cognition" (VLM output) with "action" capabilities by conditioning the diffusion-bait based action module on the VLM's extracted features, addressing the continuous, multimodal, and temporally correlated nature of robot actions.
CogACT:
no: This model is a prototype generalist robot policy developed by prior research to address the versatility gap between human and machine intelligence. Built upon a pre-trained vision-language model to harness Internet-scale image-text corpora for semantic reasoning and problem-solving, Ro is extended into VLA model through cross-embodiment training, integrating diverse data from various robot types enhance generalization and mitigate data scarcity. Featuring an action chunking architecture with flow matching, enables high-frequency control up to 50 Hz for dexterous tasks such laundry folding, and is pre-trained on over 10,000 hours of robot data before fine-tuning on curated datasets to optimize dexterity, efficiency, and robustness, serving as robust reference for evaluating our proposed Efficient VLA framework.
VI.A-Cache: This is training-fres acceleration method designed improve VLA model efficiency in robotic manipulation. VLA-Cache operates on the principle that visual inputs in
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sequential robotic tasks often exhibit minimal variation between successive steps, particularly in background regions. incorporates token-selectien mechanism that identifies visually static tokens with minimal changes from the previous step and reuses their computational results via K VV-achee Additionally, includes fine-grained selection scheme to filter out critical task-relevant tokens, ensuring they undergo full computation to preserve accuracy, and employs a layer-adaptive strategy to adjust reuse ratios based on attention concentration.
Fast This approach focuses on accelerating inference in Large Vision-Language Models (LVLMs) by pruning redundant visual tokens. FastV's core insight is the identification of inefficient attention phenomena in deeper layers of popular LVLMs, where image tokens receive significantly less attention despite accounting for a large portion of input tokens. proposes a plug-amn -aduga method that dynamically prunes a percentage of these less impact- ful visual tokens after a specific layer, guided by attention scores, to reduce computational costs (FLOPs) without sacrificing performance.
B More Experimental Results
B.1 Results on Vision-Languaage Tasks
Table 6 illustrates the performance of our EfficientVLA token pruning strategy in comparison to the vanilla LLaVA-1.5-7B baseline and the conventional FastV method across suite of multimodal vision-language benchmarks. Our approach, which employs an advanced token pruning mechanism tailored to preserve semantically critical visual tokens while aggressively reducing redundancy, consistently outperforms FastV at equivalent token retention levels. Specifically, at 192 tokens, Effi- cientVLA achieves an average performance 98.0%, retaining nearly all baseline efficacy, whereas FastV drops to 91.2%; similarly, at 128 tokens, our method yields 96.6% average accuracy, markedly superior to FastV'ss 86.4%. This superior retention of multimodal understanding capabilities under- scores EfficientVLA's efficacy in mitigating information loss during pruning, enabling substantial inference acceleration in vision-lange ssionnlanguage tasks without compromising downstream task performance.
Table 6: Performance evaluation of the Efficient token pruning strategy compared to the vanilla LLaVA-1.5-7B baseline and the traditional FastV method across diverse multimodal vision-lang langugee benchmarks.
Benchmarks
Method
GQA
MMB
MMB--C
MME
POPE
SQA VQA-22
VQA-Text
VizWiz
OCRBench
Avg
LLaVA-L.5 1.5-7B (Vanilla, 576 tekens)
61.9
64.7
58.1
1862
35.9
69.5
78.5
58.2
50.0
297
100.0%
(192 tokens)
61.2
57.0
1612
64.8
67.3
50.8
291
91.2%
Ours (192 tokens)
58.5
62.3
$7.0
1823
8288
69.6
76.7
57.6
$1.0
292
98 0%.0%
FFsttV (128 tokens)
29.4
56.1
564
1490
56.0
60.2
61.8
50.6
51.3
285
86.4%
+Ours (128 tokens)
58.2
61.5
56.6
1763
79.8
68.5
75.7
56.1
51.4
289
96.6%
B.2 More Generalizability Experiments
To validate the generalizability of our proposed method, we conducted additional experiments on the model using the LIBERO 43) benchmark, fine-tuning it for 30k steps baseline and performing inference on a NVIDIA 4090. The LIBERO benchmark evaluates lifelong robotic manipulation in four challenging task suites, LIBERO-Spatial, LIBERO-Object, LIBERO-Goal and LIBERO-10. introducing variations in spatial layouts, object selection, task goals, and long- horizoo planning. which test a model's ability to generalize across diverse manipulation scenarios. As shown in Table our training-free ffree approach was evaluated on these four tasks. Random Dropping, which randomly prunes 112 visual tokens, achieved success rate of only 18.5% with limited acceleration (1.14x) FastV provided a modest 1.16x speedup but incurred performance loss. In contrast, EfficientVLA without pruning already achieved a 1.46 x speedup with only 1.8% performance drop. With pruning, Efficcint VLA delivered 1.71 x speedup, increasing inference frequency from 11.65 Hz to 19.96 Hz, while outperforming FastV in success rate. These results demonstrate the method's robustness and scalability across diverse VLA models and datasets.
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Table 7: Quantitative assessment of performance and efficiency metrics for the mo baseline model and comparative methods on the LIBERO benchmark, evaluating task-specific success rates (Spatial, Object, Goal, 10), average accuracy, latency, speedup, and operational frequency.
Method
Spatial
Object
Goal
10
Avg.
Latency/s
Speedup Freq. (Hz)
98.2
98.2
94
83.2
93.4
0,0858
11.65
Random Dropping
36.8
21.4
15.8
0
18.5
0.0753
1.14x
13.28
FastV
95.2
96.6
90.2
79.2
90.3
0.0739
1.16x
13.53
Efficient VI. w/o prune
95.8
97.8
91.6
81.2
91.6
0.0588
1,46x
17.01
AfficientVLA
95.2
97.2
90.6
78.6
90.4
0.0501
1.71x
19.96
C Related Work on Inference Acceleration Techniques
variety of training-free methods have emerged to expedite inference in large-scale models by leveraging layer pruning, token pruning, and temporal caching mechanisms, thereby reducing compu- tational overhead without necessitating model retraining. the context of layer pruning for large language models (LLMs), Yuan al. [45] mitigate redundancy by collapsing multiple redundant layers into a single effective layer, preserving essential functionalities; SlimGPT (46) applies batched greedy pruning coupled with incremental pruning ratios to systematically minimize error accumu- lation during compression; and TrimLLM [47 enables progressive and targeted layer dropping by exploiting layer-wist specialization, allowing for adaptive model thimning tailored to specific tasks. For visual token pruning in large vision-langee languuge models (LVLMs), FastV 42) executes dynamic token elimination guided by real-time attention scores streamline processing: Sparse VLM [48] introduces a ext-guuied paradigm for lank-based pruning. further enhanced by token recycling to retain informative elements; PruMerge [49] adaptively merges redundant tokens by capitalizing inherent attention sparsity patterns; and MustDrop [50] refines token selection and retention strategies across distinct phases including encoding, prefilling, and decoding, ensuring minimal performance degradation. Regarding temporal caching in diffusion transformers, FORA [51] supports seamless direct reuse of cached intermediate features in training-free manner, accelerating iterative generation processes; while ToCa [52] employs token-wise caching with dynamically adaptive retention ratios, optimizing memory usage and inference speed across varying sequence lengths.
D Impact Statement
This paper introduces EfficientVLA, crucial contribution to Vision-Langgage-AAttonn (VLA) models by directly addressing their significant computational and memory demands through a novel training- free framework. EfficientVLA synergistically prunes redundant language layers, optimizes visual token selection for task-relevance and diversity, and caches intermediate features in the diffusion- based action head, thereby significantly boosting VLA model efficiency and speed. This enables the practical deployment of powerful VLA models on resource-constraing robotic platforms for real- time interaction, accelerating progress in robotic manipulation and reasoning tasks, and contributing to more development by reducing computational load. We intend this work for ethical academic research and authorized commercial applications and strictly prohibit its use for harmful, unethical, or unlawful robotic actions, underscoring our responsibility to ensure societal benefit aligned with fundamental ethical principles.
E
Limitations
While EfficientVLA significantly advances VLA model acceleration, certain limitations warrant discussion. Our training-free approach, may not achieve the maximal compression or speedup attainable by training-aware methods. The fixed cache interval N in action head introduces trade-off between acceleration and action fidelity and future work could explore adaptive caching. Furthermore, due the limited availability of open-source diffusion-based VLAA models, our current demonstrations are primarily on CogACT. In the future, aim to validate scalability and effective-
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ness of EfficientVLA across a wider range of models and tasks as more such architectures become available.
24


Reviews:
[{"review_id": "Cmn3iw47d6", "reviewer": "Reviewer_NXEq", "summary": "This paper investigates methods to accelerate the inference speed of vision-language-action (VLA) models using structured and training-free approaches. It employs three techniques—pruning, dynamic visual token selection, and caching/reusing key features—to achieve acceleration. The proposed approach outperforms CogACT and other baselines on the SIMPLER benchmark.", "questions": "Additional experiments using pi0 would strengthen the evaluation. A comparison with Otter regarding visual token pruning is recommended. Conducting real-world robotic experiments would also enhance the validation of the approach.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The investigated problem is significant and relevant. Employing a training-free acceleration method for VLA models is meaningful, particularly given that speed is crucial in robot learning.\n2. The proposed method is reasonable, and the paper is clearly written and easy to follow.\n3. The analysis presented is insightful and intuitive.\n4. The ablation study conducted is thorough and comprehensive. Code availability facilitates reproducibility.\n\nWeaknesses:\n\n1. CogACT is not a representative VLA model. Its baseline performance is low, making it less ideal for this study. A model like pi0 would have been a more suitable choice.\n\n2. Evaluating robot learning solely based on task completion overlooks other crucial aspects, such as motion smoothness. Does EfficientVLA predict the 6D pose or joint positions in the SIMPLER environment? If the output changes, can EfficientVLA maintain smoothness?\n\n3. FLOPs and parameter counts are not the most critical metrics for evaluating model efficiency in robotics. A more relevant metric would be how many actions the model can predict per second given the same chunk size.\n\n4. A comparison with Otter [1], a closely related study that also leverages CLIP for visual token reduction, is necessary.\n\n[1] Otter: A vision-language-action model with text-aware visual feature extraction.", "was_revised": true, "final_justification": "The rebuttal address my concerns, and other reviewer also agree with the acceptance, therefore i maintain my current score.", "rebuttal": "Thank you very much for your positive feedback and constructive suggestions. Your professional advice has been invaluable in further improving our work!\n\n**Weakness**:\n\n***W1/Q1:** A model like pi0 would have been a more suitable choice.*\n\n**A1:** Thank you for your valuable suggestion. To validate the generalizability of our proposed method, we conducted additional experiments on the $\\pi_0$ model using the LIBERO on a NVIDIA 4090. \n\nAs shown in Table 1, our training-free approach was evaluated on these four tasks. Random Dropping achieved a success rate of only 18.5% with limited acceleration. FastV provide a modest 1.16X speedup but incurred performance loss. In contrast, EfficientVLA without pruning already achieves a 1.46X speedup with only a 1.8% performance drop. With pruning, EfficientVLA delivered a 1.71X speedup, increasing inference frequency from 11.65 Hz to 19.96 Hz, while outperforming FastV in success rate. These results demonstrate EfficientVLA’s robustness and scalability across diverse models and datasets. Further experiments on additional model and benchmarks are planned for future work. \n\n**Table 1**: Performance and efficiency metrics of EfficientVLA and baselines on LIBERO tasks.\n\n| LIBERO                 | Spatial | Object | Goal | 10   | Avg. | Latency/s↓ | Speedup↑ | Frequency↑ (Hz) |\n| ---------------------- | ------- | ------ | ---- | ---- | ---- | ---------- | -------- | --------------- |\n| $\\pi_0$                | 98.2    | 98.2   | 94   | 83.2 | 93.4 | 0.0858     | -        | 11.65           |\n| Random Dropping        | 36.8    | 21.4   | 15.8 | 0    | 18.5 | 0.0753     | 1.14X    | 13.28           |\n| FastV                  | 95.2    | 96.6   | 90.2 | 79.2 | 90.3 | 0.0739     | 1.16X    | 13.53           |\n| EfficientVLA w/o prune | 95.8    | 97.8   | 91.6 | 81.2 | 91.6 | 0.0588     | 1.46X    | 17.01           |\n| EfficientVLA           | 95.2    | 97.2   | 90.6 | 78.6 | 90.4 | 0.0501     | 1.71X    | 19.96           |\n\n***W2:** Evaluating robot learning solely based on task completion overlooks other crucial aspects, such as motion smoothness. Does EfficientVLA predict the 6D pose or joint positions in the SIMPLER environment? If the output changes, can EfficientVLA maintain smoothness?*\n\n**A2:** Thank you for your insightful comments regarding the evaluation of motion smoothness in robot learning, which is indeed a critical aspect beyond task completion. \n\n**EfficientVLA Output:** EfficientVLA predicts 6D end-effector poses in the SIMPLER environment, consisting of a 3D position and a 3D rotation in axis-angle representation, following the setting of CogACT. These outputs are processed by the SIMPLER's system identification module , which maps the predicted 6D poses to joint positions via inverse kinematics. \n\n**Motion Smoothness Evaluation:**  Due to the policy of this rebuttal, we cannot provide visualization resources and we will include execution trajectory video frames in later versions of the paper. So in order to address your concern about motion smoothness, we implemented metrics for acceleration smoothness and joint jerk to quantify the smoothness of trajectories.\n\nWe collected data from the ManiSkill2, including end-effector pose, joint velocities , and the control frequency time step.\n\nWe computed:\n\n1. **Acceleration Smoothness**: \n\n   $$ \\text{Smoothness} = \\frac{1}{T-2} \\sum_{t=2}^{T-1} \\left( |\\ddot{\\mathbf{x}}_t|_2^2 + |\\dot{\\omega}_t|_2^2 \\right) $$\n\n   - Linear acceleration ( $\\ddot{\\mathbf{x}}_t \\in \\mathbb{R}^3$ ): Second-order central differencing on positions.\n   - Angular acceleration ( $\\dot{\\omega}t \\in \\mathbb{R}^3$ ).\n\n2. **Joint Jerk**: $$ \\text{Jerk} = \\frac{1}{T-2} \\sum_{t=2}^{T-1} |\\dot{v}_{\\text{arm}, t}|_2^2 $$\n\n   - Joint acceleration ( $\\dot{v}_{\\text{arm}, t} \\in \\mathbb{R}^{nq}$ ): Second-order central differencing on joint velocities.\n\nWe tested CogACT and EfficientVLA on two tasks, with results as follows: For PickCan, EfficientVLA shows smoothness and jerk comparable to CogACT; for MoveNear, EfficientVLA exhibits similar smoothness and slightly lower jerk than CogACT. These results highlight that EfficientVLA maintains motion smoothness consistent with CogACT, supporting its efficacy in robotic tasks. \n\n**Table 2:** Motion smoothness metrics for CogACT and EfficientVLA on PickCan and MoveNear tasks in SIMPLER.\n\n|              | PickCan    | PickCan | MoveNear   | MoveNear |\n| ------------ | ---------- | ------- | ---------- | -------- |\n|              | Smoothness | Jerk    | Smoothness | Jerk     |\n| CogACT       | 0.0079     | 1.0962  | 0.0053     | 1.5205   |\n| EfficientVLA | 0.0094     | 1.1254  | 0.0051     | 1.4306   |\n\n***W3:** A more relevant metric would be how many actions the model can predict per second given the same chunk size.*\n\n**A3:** Thank you for pointing this out. We agree that the number of actions predicted per second is a more relevant metric for evaluating model efficiency in robotics. In the SIMPLER, with a chunk size of 16 (default for CogACT), we conducted experiments on a NVIDIA A40. As shown in Table 3, EfficientVLA achieves 8–9 actions per second, significantly outperforming CogACT, which predicts 4–5 actions per second, demonstrating our superior acceleration. In comparison, VLA-Cache achieves 5–6 actions per second. Moreover, EfficientVLA reduces computational load to 28.9% of VLA-Cache’s, with only a 0.6% performance drop, highlighting its efficiency and practical applicability. \n\n**Table 3:**  Performance and efficiency metrics of EfficientVLA and baselines on SIMPLER tasks.\n\n|              | PickCan | MoveNear | Drawer | DrawerApple | Average | Flops↓（%） | Frequency↑ (Hz) |\n| ------------ | ------- | -------- | ------ | ----------- | ------- | ----------- | --------------- |\n| CogACT       | 91.3    | 85       | 71.8   | 50.9        | 74.8    | 100         | 4.2             |\n| VLA-Cache    | 92      | 83.3     | 70.5   | 51.6        | 74.4    | 80.1        | 5.6             |\n| EfficientVLA | 93.3    | 81.3     | 68.2   | 53.8        | 74.2    | 28.9        | 8.3             |\n\n***W4/Q1:** A comparison with Otter, a closely related study that also leverages CLIP for visual token reduction, is necessary.*\n\n**A4:**  Thank you for suggesting a comparison with OTTER, which also uses CLIP for visual token reduction. Below is a concise comparison highlighting EfficientVLA's distinctions. \n\n- **Compression Scope**: OTTER compresses m visual tokens to 1 using text-guided queries, while EfficientVLA retains text tokens and prunes visual tokens dynamically based on task relevance and image redundancy.\n- **Scope of Token Compression:** OTTER reduces visual tokens and compresses text tokens into a single representation via cross-attention pooling before feeding them into the policy network. In contrast, EfficientVLA only prunes visual tokens, keeping the original text token representation throughout the pipeline.\n- **Timing**: OTTER uses cross-attention pooling to compress visual features into a single token early in the pipeline before feeding them into the policy network. In contrast, EfficientVLA delays pruning to the LLM inference stage, starting in the initial transformer layers.\n- **Training Dependency**: OTTER employs a frozen CLIP model, requiring end-to-end training to optimize query vectors for cross-attention pooling, capturing semantics but limiting adaptability. EfficientVLA, however, is training-free, using the CogACT backbone for adaptive token pruning.\n- **Strategy**: OTTER employs cross-attention pooling with learnable queries to compress m visual tokens into one, guided by text-aware semantics. EfficientVLA uses a task-aware, two-step strategy: selecting key task-aligned tokens via a similarity metric, then adding diverse tokens, analyzing feature contributions for adaptive, training-free pruning.\n\nWe also implemented OTTER's token pruning strategy (denoted as \"w/ Otter-T\") and EfficientVLA's pruning strategy (denoted as \"EfficientVLA (only T)\") on the CogACT baseline, alongside the full EfficientVLA.\n\n**Table 4:**  Comparison of Visual Token Pruning Strategies on CogACT in SIMPLER.\n\n|                       | PickCan | MoveNear | Drawer | DrawerApple | Average | Frequency↑(Hz) |\n| --------------------- | ------- | -------- | ------ | ----------- | ------- | -------------- |\n| CogACT                | 91.3    | 85       | 71.8   | 50.9        | 74.8    | 4.2            |\n| w/ Otter-T            | 83.7    | 77.9     | 57.1   | 26.8        | 61.4    | 5.6            |\n| EfficientVLA (only T) | 95      | 85.8     | 69.9   | 49.6        | 75.1    | 5.2            |\n| EfficientVLA          | 93.3    | 81.3     | 68.2   | 53.8        | 74.2    | 8.3            |\n\n**Questions:**\n\n***Q1:** Additional experiments using pi0 would strengthen the evaluation. A comparison with Otter regarding visual token pruning is recommended. **Conducting real-world robotic experiments would also enhance the validation of the approach**.*\n\n**A1:**  Thank you for your suggestion on real-world robotic experiments to enhance validation. Our focus is accelerating policy inference, reliably evaluated in simulation, where EfficientVLA’s core mechanism remains consistent with real-world deployment. Resource and time constraints limit real-world tests during rebuttal, but we report latency on GPUs (e.g., 3090, 4090, due to space limitations, please see Table 2 of the reply to Reviewer 9KWi), standard in robotics, achieving 8–17 Hz for real-time tasks. We believe our analysis supports real-world viability and commit to updating the paper with real-robot validation upon completion, welcoming your continued attention."}, {"review_id": "9OATWsIdB7", "reviewer": "Reviewer_9KWi", "summary": "This paper presents EfficientVLA, a training-free, structured inference acceleration framework for diffusion-based Vision-Language-Action (VLA) models. The method systematically addresses inefficiencies across the VLA pipeline via three key components: (1) non-contiguous pruning of redundant layers in the language module based on inter-layer similarity; (2) task-aware and diversity-promoting visual token pruning; and (3) temporal feature caching within the diffusion-based action head. Experiments on the CogACT model in the SIMPLER environment show up to 1.93× inference speedup and 71.1% FLOPs reduction with negligible accuracy loss (<0.6%).", "questions": "- Do redundancies in different modules interact with each other? For example, does pruning in the language module influence token selection or the effectiveness of diffusion caching? Is there a trade-off involved when reducing redundancy across different stages of the VLA pipeline?\n\n- Can EfficientVLA generalize to other multimodal tasks beyond robotics? In particular, could the techniques for visual token pruning and temporal caching be applied to VQA, image captioning, or other vision-language domains?", "limitations": "Yes.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths:**\n- The paper is well-motivated, with a bottleneck analysis (LLM memory, visual redundancy, and diffusion-step redundancy) that justifies a holistic acceleration framework.\n\n- EfficientVLA is training-free, making it easily deployable across existing VLA models without retraining.\n\n- The method effectively reduces redundancies across the VLA pipeline by combining layer pruning, task-relevant and diverse visual token selection, and feature caching in diffusion-based action heads.\n\n**Weakness:**\n\n- More experiments are encouraged to validate the generalizability of the proposed method, especially on other VLA models and dataset such as Libro. Furthermore, the SIMPLER benchmark includes only four tasks, limiting the evaluation's diversity.\n\n- Real-world robotic experiments would strengthen the claims regarding deployment efficiency, especially in terms of latency and robustness under hardware constraints.\n\n- It is encouraged to include related works with respect to existing techniques in layer pruning, token pruning, and temporal caching.", "was_revised": true, "final_justification": "The authors have addressed some of my concerns. However, the lack of real-world demonstration may be a potential weakness of the work. Moreover, it is encouraged to add the related works to the main paper.", "rebuttal": "Thank you very much for your thoughtful and valuable feedback. We have carefully reviewed and addressed the concerns you raised in your list to the best of our ability.\n\n**Weakness:**\n\n***W1:** More experiments are encouraged to validate the generalizability of the proposed method, especially on other VLA models and dataset such as LIBERO.*\n\n**A1:** Thank you for your valuable suggestion. To validate the generalizability of our proposed method, we conducted additional experiments on the $\\pi_0$ model using the LIBERO on a NVIDIA 4090. \n\nAs shown in Table 1, our training-free approach was evaluated on these four tasks. Random Dropping achieved a success rate of only 18.5% with limited acceleration. FastV provide a modest 1.16X speedup but incurred performance loss. In contrast, EfficientVLA without pruning already achieves a 1.46X speedup with only a 1.8% performance drop. With pruning, EfficientVLA delivered a 1.71X speedup, increasing inference frequency from 11.65 Hz to 19.96 Hz, while outperforming FastV in success rate. These results demonstrate EfficientVLA’s robustness and scalability across diverse models and datasets. Further experiments on additional model and benchmarks are planned for future work. \n\n**Table 1**: Performance and efficiency metrics of EfficientVLA and baselines on LIBERO benchmark tasks.\n\n| LIBERO                 | Spatial | Object | Goal | 10   | Avg. | Latency/s↓ | Speedup↑ | Frequency↑ (Hz) |\n| ---------------------- | ------- | ------ | ---- | ---- | ---- | ---------- | -------- | --------------- |\n| $\\pi_0$                | 98.2    | 98.2   | 94   | 83.2 | 93.4 | 0.0858     | -        | 11.65           |\n| Random Dropping        | 36.8    | 21.4   | 15.8 | 0    | 18.5 | 0.0753     | 1.14X    | 13.28           |\n| FastV                  | 95.2    | 96.6   | 90.2 | 79.2 | 90.3 | 0.0739     | 1.16X    | 13.53           |\n| EfficientVLA w/o prune | 95.8    | 97.8   | 91.6 | 81.2 | 91.6 | 0.0588     | 1.46X    | 17.01           |\n| EfficientVLA           | 95.2    | 97.2   | 90.6 | 78.6 | 90.4 | 0.0501     | 1.71X    | 19.96           |\n\n***W2:** Real-world robotic experiments would strengthen the claims regarding deployment efficiency, especially in terms of latency and robustness under hardware constraints.*\n\n**A2:** Thank you for your insightful suggestion. Our primary focus in this work is to accelerate policy inference, and this aspect can be thoroughly and reliably evaluated in simulation. Notably, the core inference mechanism of our model remains unchanged between simulation and real-world deployment, making simulation-based evaluation a strong proxy for assessing inference efficiency. \n\nWhile we don’t have enough time and resources to set up real-world experiments within the rebuttal period, we aim to demonstrate the real-world applicability of our method by reporting policy inference latency on widely-used GPUs for robotic research, including the 3090 and 4090 (see Table 2). These GPUs are standard in many real-robot systems [1,2,3,4].  \n\nOur EfficientVLA achieves a control frequency of approximately 8–17 Hz on these platforms, which is sufficient for real-time closed-loop control in many manipulation tasks. Regarding additional real-world system latencies, such as delays from camera or proprioception sensors, or robot controller execution, these are typically addressed using established engineering techniques like latency compensation, asynchronous execution[5], and real-time action chunking [6]. These concerns, while important in practice, are orthogonal to the core contribution of our paper and are thus considered out of scope for this work. We hope our experimental setup and hardware-level analysis adequately support the real-world viability of EfficientVLA, and we plan to include real-robot validation in future work.\n\n **Table 2:** Latency (seconds) and Speedup of EfficientVLA compared to CogACT on various GPUs.\n\n|              | 3090(BF16) | 4090(BF16) | A40    | A100   | A6000  |\n| ------------ | ---------- | ---------- | ------ | ------ | ------ |\n| CogACT       | 0.1953     | 0.1148     | 0.2342 | 0.1883 | 0.2235 |\n| EfficientVLA | 0.1022     | 0.0588     | 0.1213 | 0.0946 | 0.1194 |\n| Speedup↑     | 1.91X      | 1.95X      | 1.93X  | 1.99X  | 1.87X  |\n\n***W3:** It is encouraged to include related works with respect to existing techniques in layer pruning, token pruning, and temporal caching.*\n\n**A3:** Thank you for your insightful suggestion. In layer pruning for LLMs, Yuan et al. [7] explore reducing layers to a single layer to address redundancy, while ShortGPT [8] uses a Block Influence metric to remove redundant layers, and SlimGPT [9] applies Batched Greedy Pruning with Incremental Pruning Ratio to reduce error accumulation. TrimLLM [10] employs layer-wise specialization for progressive layer dropping.\n\nFor visual token pruning in Large Vision-Language Models, FastV [11] dynamically prunes tokens using attention scores, SparseVLM [12] offers a text-guided approach with rank-based pruning and token recycling, PruMerge [13] adaptively merges tokens based on attention sparsity, and MustDrop [14] refines tokens across encoding, prefilling, and decoding stages.\n\nIn diffusion caching for Diffusion Transformers, FORA [15] reuses feature outputs without retraining, ToCa [16] applies token-wise caching with adaptive ratios, and DuCa [17] integrates aggressive and conservative caching with V-caching, all training-free.\n\n**Questions**\n\n**Q1:** Interaction and Trade-offs of Redundancy Reduction in VLA Modules\n\n**A1:** Thank you for your insightful question regarding the interaction of redundancies across different modules and potential influences. The ablation study in Table 5 of the paper provides evidence of these interactions within our EfficientVLA framework.\n\nThe results suggest that redundancies across modules are interdependent. Visual token pruning alone may improve performance over the baseline, indicating effective redundancy removal. However, combining it with layer pruning reduces performance, suggesting language module pruning may alter token selection context, diminishing retained token relevance. Similarly, action caching alone offers moderate speedup, but pairing it with model compression enhances efficiency.\n\nRegarding trade-offs, our experiments indicate a safe acceleration range for each module. For example, visual token pruning, reducing 50% to 70% of tokens, incurs negligible or no performance loss and sometimes improves outcomes for most tasks. For action timestep feature caching, recomputing caches every 2 to 4 timesteps maintains efficacy across diverse scenarios. This configuration is robust for direct use across tasks or models with default settings. For higher acceleration, we recommend cautiously increasing each module’s pruning ratio, with adjustments tailored to specific needs. Due to space constraints, We will elaborate on these findings in the revised manuscript.\n\n**Q2:** Generalization of EfficientVLA's techniques to Multimodal Vision-Language Tasks\n\n**A2:** Thank you for the valuable suggestion. To address this, we conducted experiments applying EfficientVLA’s token pruning method to vision-language tasks using the LLaVA-1.5-7B model across multiple benchmarks. \n\nThe results, presented in Table 3, demonstrate that our approach generalizes effectively to these domains. By retaining only 22.2% of visual tokens, we achieved 96.6% of the original performance, significantly outperforming FastV. \n\n**Table 3:** Performance comparison of LLaVA-1.5-7B with vanilla, FastV, and our token pruning method on various  benchmarks.\n\n|                                        | GQA  | MMB  | MMB-CN | MME  | POPE | SQA  | VQA-V2 | VQA-Text | VizWiz | OCRBench | Avg    |\n| -------------------------------------- | ---- | ---- | ------ | ---- | ---- | ---- | ------ | -------- | ------ | -------- | ------ |\n| **LLaVA-1.5-7B**（Vanilla，576tokens） | 61.9 | 64.7 | 58.1   | 1862 | 85.9 | 69.5 | 78.5   | 58.2     | 50     | 297      | 100%   |\n| **+FastV**（192 tokens）               | 52.7 | 61.2 | 57     | 1612 | 64.8 | 67.3 | 67.1   | 52.5     | 50.8   | 291      | 91.20% |\n| **+Ours**（192 tokens）                | 58.5 | 62.3 | 57     | 1823 | 82.8 | 69.6 | 76.7   | 57.6     | 51     | 292      | 98.00% |\n| **+FastV**（128 tokens）               | 49.4 | 56.1 | 56.4   | 1490 | 59.6 | 60.2 | 61.8   | 50.6     | 51.3   | 285      | 86.40% |\n| **+Ours**（128 tokens）                | 58.2 | 61.5 | 56.6   | 1763 | 79.8 | 68.5 | 75.7   | 56.1     | 51.4   | 289      | 96.60% |\n\n[1] RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation\n\n[2] Data Scaling Laws in Imitation Learning for Robotic Manipulation\n\n[3] OpenVLA: An Open-Source Vision-Language-Action Model\n\n[4] Reactive diffusion policy: Slow-fast visual-tactile policy learning for contact-rich manipulation\n\n[5] Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots\n\n[6] Real-Time Execution of Action Chunking Flow Policies\n\n[7] Why lift so heavy? slimming large language models by cutting off the layers\n\n[8] Shortgpt: Layers in large language models are more redundant than you expect\n\n[9] Slimgpt: Layer-wise structured pruning for large language models\n\n[10] TrimLLM: Progressive Layer Dropping for Domain-Specific LLMs\n\n[11] An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models\n\n[12] Sparsevlm: Visual token sparsification for efficient vision-language model inference\n\n[13] Llava-prumerge: Adaptive token reduction for efficient large multimodal models\n\n[14] Multi-stage vision token dropping: Towards efficient multimodal large language model\n\n[15] Fora: Fast-forward caching in diffusion transformer acceleration\n\n[16] Accelerating diffusion transformers with token-wise feature caching\n\n[17] Accelerating diffusion transformers with dual feature caching"}, {"review_id": "JbmLZt3vsT", "reviewer": "Reviewer_nRSS", "summary": "This paper aims to improve the inference efficiency of pretrained VLA models and introduces a training-free framework named EfficientVLA. The proposed efficiency improvements are achieved through three key techniques: selective vision token pruning, LLM layer pruning, and action head computation caching. Compared to the vanilla model, EfficientVLA achieves a 1.93× inference speedup under the SIMPLER setting, with only a 0.6% drop in performance.", "questions": "1. Based on Table 5, visual token pruning appears to improve visual success rate, whereas model compression and action cache mainly reduce computational costs. Could the authors elaborate on this finding and provide further discussion?\n2. Regarding the action head caching, could the authors clarify how feature updates are handled when self-attention and MLP layers are bypassed? An explicit explanation of the feature flow in this case would greatly help reader comprehension.", "limitations": "While the technical novelty of the proposed methods is limited, since similar techniques have been explored in the VLM or LLM domains, their integration into the VLA context is also important. The paper effectively adapts existing ideas to a new domain, offering meaningful efficiency improvements.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n1. The redundancy analysis presented in Figure 1 provides valuable insight into the computational inefficiency in VLA models.\n2. The proposed framework achieves state-of-the-art inference efficiency with minimal performance degradation, demonstrating the effectiveness of combining the three techniques.\n\n**Weaknesses**\n1. The framework is only evaluated on the CogACT model. Experiments on additional VLA architectures would strengthen the generalizability and robustness of the method.\n2. More ablations on visual token reduction are needed. For instance, analyzing the individual contributions of Vkey, Vtask, and Vdiv would clarify their roles. Additionally, visualizations of the token pruning process would improve interpretability.\n\n**Minor**\n1. It seems that $r_i$ in Equation (2) and $s_i$ in Section 3.3.2 refer to the same variable. Please clarify or correct if this is a typo.\n2. In Table 4, is the ratio shown the token reduction ratio or the token preservation ratio? Also, why is the last column showing a 100% ratio while its FLOPs are the highest among all columns?", "was_revised": true, "final_justification": "I thank the authors for their detailed response, which has clearly addressed my concerns. Given the paper’s broad relevance to the VLA community, I am raising my score to accept.", "rebuttal": "We extend our heartfelt thanks to the reviewer for their thorough and constructive input, which has been instrumental in shaping this work.\n\n**Weaknesses** \n\n***W1:** The framework is only evaluated on the CogACT model. Experiments on additional VLA architectures would strengthen the generalizability and robustness of the method.*\n\n**A1:** Thank you for your valuable suggestion. To validate the generalizability of our proposed method, we conducted additional experiments on the $\\pi_0$ model using the LIBERO benchmark, fine-tuning it for 30k steps as a baseline and performing inference on a NVIDIA 4090. The LIBERO benchmark evaluates lifelong robotic manipulation across four challenging task suites—LIBERO Spatial, LIBERO-Object, LIBERO-Goal, and LIBERO-10—introducing variations in spatial layouts, object selection, task goals, and long-horizon planning, which test a model’s ability to generalize across diverse manipulation scenarios.\n\nAs shown in Table 1, our training-free approach was evaluated on these four tasks. Random Dropping, which randomly prunes 112 visual tokens, achieved a success rate of only 18.5% with limited acceleration (1.14X). FastV provided a modest 1.16X speedup but incurred performance loss. In contrast, EfficientVLA without pruning already achieved a 1.46X speedup with only a 1.8% performance drop. With pruning, EfficientVLA delivered a 1.71X speedup, increasing inference frequency from 11.65 Hz to 19.96 Hz, while outperforming FastV in success rate. These results demonstrate the method’s robustness and scalability across diverse VLA models and datasets. Addressing the SIMPLER benchmark’s limited diversity, LIBERO’s broader scope strengthens our evaluation, and further experiments on additional benchmarks are planned for future work. We appreciate your suggestion, which has helped make our work more complete.\n\n**Table 1:** Performance and efficiency metrics of EfficientVLA and baselines on LIBERO benchmark tasks.\n\n| LIBERO                 | Spatial | Object | Goal | 10   | Avg. | Latency/s↓ | Speedup↑ | Frequency↑ (Hz) |\n| ---------------------- | ------- | ------ | ---- | ---- | ---- | ---------- | -------- | --------------- |\n| $\\pi_0$                | 98.2    | 98.2   | 94   | 83.2 | 93.4 | 0.0858     | -        | 11.65           |\n| Random Dropping        | 36.8    | 21.4   | 15.8 | 0    | 18.5 | 0.0753     | 1.14X    | 13.28           |\n| FastV                  | 95.2    | 96.6   | 90.2 | 79.2 | 90.3 | 0.0739     | 1.16X    | 13.53           |\n| EfficientVLA w/o prune | 95.8    | 97.8   | 91.6 | 81.2 | 91.6 | 0.0588     | 1.46X    | 17.01           |\n| EfficientVLA           | 95.2    | 97.2   | 90.6 | 78.6 | 90.4 | 0.0501     | 1.71X    | 19.96           |\n\n\n\n***W2:** More ablations on visual token reduction are needed. For instance, analyzing the individual contributions of Vkey, Vtask, and Vdiv would clarify their roles. Additionally, visualizations of the token pruning process would improve interpretability.*\n\n**A2:** Thank you for your valuable suggestions. We appreciate the call to analyze the individual contributions of Vkey, Vtask, and Vdiv to clarify their roles. \n\n- Vkey is selected as the top $K_ {key}$ tokens (empirically set between 4 and 8) based on normalized task relevance scores $s_i$, derived from cross-attention scores  $A^{(h)}_ {i,j}$ averaged across H heads and summed over $L_ {ctx}$ contextual elements. This set forms the core visual cues essential for task understanding and execution, providing a foundational scaffold for $V_ {pruned}$.\n\n- Vtask, selecting $K_ {task} = \\lfloor \\alpha \\cdot K_ {aug} \\rfloor$ high-relevance tokens from the remaining $V_{rem}$, enhances task-oriented semantics by refining details that Vkey might overlook. This step leverages the hybrid selection strategy guided by the parameter $\\alpha$, ensuring alignment with language instructions and improving task-specific performance.\n\n- Vdiv, choosing $K_ {div} = K_ {aug} - K_ {task}$ tokens based on cosine distance, boosts diversity and robustness to complex or unseen scenarios by incorporating distinct visual features. This diversity-driven augmentation, critical for generalization across varied environments, mitigates overfitting to core tokens. We will expand on these contributions with further analysis in the revised manuscript.\n\nWe conducted further ablations on \"PickCan\" with token pruning only, retaining 56 tokens. Table 2 explores Vkey’s impact with Vtask:Vdiv = 1:1, showing suboptimal success rates (SR) when Vkey is overly narrow or excessive, with an optimal range around 4-8 tokens. In Table 3, with Vkey = 4, we varied Vtask:Vdiv ratios, revealing a need to balance task relevance and diversity; a low Vdiv ratio risks overfitting, while balanced ratiosoptimize performance. \n\n**Table 2:** Ablation results for Vkey impact on \"PickCan\" success rate (SR) with 56 tokens and Vtask:Vdiv = 1:1.\n\n| Vkey  | 1    | 2    | 4    | 8    | 12   |\n| ----- | ---- | ---- | ---- | ---- | ---- |\n| SR(%) | 89.6 | 94.2 | 95   | 93.3 | 91.3 |\n\n**Table 3:** Ablation results for Vtask:Vdiv ratio impact on \"PickCan\" success rate (SR) with 56 tokens and Vkey = 4.\n\n| Vtask：Vdiv | 5 ：1 | 2 ：1 | 1：1 | 1： 2 | 1 ：5 |\n| ----------- | ----- | ----- | ---- | ----- | ----- |\n| SR(%)       | 88    | 91.3  | 95   | 94.6  | 94.2  |\n\nDue to rebuttal policy constraints, we cannot provide visualizations at this stage, but we commit to including pruning process visualizations in the revised manuscript to enhance interpretability, and we appreciate the suggestion.\n\n\n\n**Minor**\n\n***M1:** It seems  that $r_i$ in Equation (2) and $s_i$ in Section 3.3.2 refer to the same variable. Please clarify or correct if this is a typo.*\n\n**A1:** Thank you for your comment. We clarify that $r_i$ in Equation (2) and $s_i$ in Section 3.3.2 are distinct variables. $r_i$ represents the raw task relevance score for each visual token $v_i$, computed by averaging attention scores $A^{(h)}_ {i,j}$ across H heads and summing over $L_ {ctx}$ contextual elements. $s_i$, however, is the normalized version of $r_i$, used for robust comparison and selection of the $K_ {key}$ tokens in $V_ {key}$. This normalization step ensures consistency in token pruning. We will enhance the notation clarity in the revised manuscript.\n\n***M2:** In Table 4, is the ratio shown the token reduction ratio or the token preservation ratio? Also, why is the last column showing a 100% ratio while its FLOPs are the highest among all columns?*\n\n**A2:** Thank you for your comments. In Table 4, the ratio is the token pruning ratio, not preservation; for 256 tokens, it should be 0. We apologize for the oversight, and thank you for pointing this out! We will clarify this in the updated table and accompanying text.\n\n\n\n**Questions:**\n\n***Q1:** Based on Table 5, visual token pruning appears to improve visual success rate, whereas model compression and action cache mainly reduce computational costs. Could the authors elaborate on this finding and provide further discussion?*\n\n**A1:** Thank you for the reviewer’s insightful comment. Based on Table 5, we elaborate on the findings regarding visual token pruning, model compression, and action caching. Visual token pruning appears to enhance visual success rates for certain tasks, such as \"PickCan,\" likely due to implicit regularization that removes redundant information, thereby improving performance. This underscores the widespread presence and detrimental impact of redundancy in VLA tasks. However, for some tasks like \"MoveNear,\" pruning 77.8% of tokens reduces performance from 80.8% to 78.6%, which we attribute to task-specific characteristics influencing the necessity of certain visual cues.\n\nIn contrast, model compression and action caching primarily reduce computational costs, achieving a 1.69× acceleration. We consider this a key bottleneck in accelerating Diffusion-based VLA models. Nevertheless, visual token pruning remains essential, as it significantly lowers the overall computational load, reducing FLOPs from 100% to 42%, predominantly driven by the LLM module. These insights will be further detailed in the revised manuscript.\n\n***Q2:** Regarding the action head caching, could the authors clarify how feature updates are handled when self-attention and MLP layers are bypassed? An explicit explanation of the feature flow in this case would greatly help reader comprehension.*\n\n**A2:** Thank you for your suggestion. Regarding action head caching, we clarify the feature update process when bypassing self-attention and MLP layers in the diffusion cache. Our approach, detailed in Section 3.4, leverages the temporal coherence of intermediate features (e.g., $ h^{attn}_ t $ and $ h^{mlp}_ t $ from Equations 6 and 7) observed in the Diffusion Transformer  blocks (Figure 1(c)). At the initial timestep $ t = T_{start} $, we compute these features fully using self-attention and MLP on input $ z_t $, caching them as $ C^{attn} $ and $ C^{mlp} $. For subsequent timesteps, we recompute and update the caches at fixed intervals N (e.g., when $ t \\mod N = 0 $, per Equations 8 and 9), reusing the cached values (Equations 10 and 11) for intervening steps to bypass these computations.\n\nThis static N step caching reduces redundant calculations across T timesteps, achieving significant computational savings. Feature updates occur periodically to refresh outdated representations, balancing efficiency and action fidelity. We will include this detailed feature flow explanation in the revised manuscript to enhance comprehension."}]

Author rebuttal:

Meta-review: This paper introduces EfficientVLA, a training-free acceleration framework for Vision-Language-Action models, addressing computational inefficiencies via synergistic layer pruning in the language module, task-aware visual token selection, and temporal caching for diffusion-based action heads. The approach achieves a 1.93× inference speedup with minimal performance degradation across SIMPLER and LIBERO benchmarks, significantly reducing computational overhead. Reviewers initially raised concerns regarding limited evaluation scope, motion smoothness metrics, and comparisons with related methods like OTTER. The authors thoroughly addressed these points in their rebuttal: validating generalizability on additional models and tasks, quantifying motion stability via jerk/smoothness metrics, demonstrating superior actions-per-second throughput, and providing nuanced distinctions against contemporary techniques. All reviewers acknowledged the rebuttal’s completeness, upgrading their assessments to reflect confidence in the method's robustness, efficiency, and practical utility. Given the rigorous evaluation, scalability across tasks, and open-sourced implementation, this work offers valuable contributions toward deployable embodied AI systems and merits acceptance.
Decision: Accept (poster)