SP-VLA: A Joint Model Scheduling and Token Pruning Approach for VLA Model Acceleration
Authors: Ye Li
Yuan Meng
Zewen Sun
Kangye Ji
Chen Tang
Jiajun Fan
Xinzhu Ma
Shu-Tao Xia
Zhi Wang
Wenwu Zhu
Venue: ICLR 2026 Poster (2026)
Keywords: Vision Language Action Model
Model Lightweighting
Acceleration
Embodied intelligence
Primary area: applications to robotics, autonomy, planning

Abstract: Vision-Language-Action (VLA) models have attracted increasing attention for their strong control capabilities. However, their high computational cost and low execution frequency hinder their suitability for real-time tasks such as robotic manipulation and autonomous navigation. Existing VLA acceleration methods primarily focus on structural optimization, overlooking the fact that these models operate in sequential decision-making environments. As a result, temporal redundancy in sequential action generation and spatial redundancy in visual input remain unaddressed. To this end, we propose SP-VLA, a unified framework that accelerates VLA models by jointly scheduling models and pruning tokens. Specifically, we design an action-aware model scheduling mechanism that reduces temporal redundancy by dynamically switching between VLA model and a lightweight generator.  Inspired by the human motion pattern of focusing on key decision points while relying on intuition for other actions, we categorize VLA actions into deliberative and intuitive, assigning the former to the VLA model and the latter to the lightweight generator, enabling frequency-adaptive execution through collaborative model scheduling. To address spatial redundancy, we further develop a spatio-semantic dual-aware token pruning method.  Tokens are classified into spatial and semantic types and pruned based on their dual-aware importance to accelerate VLA inference. These two mechanisms work jointly to guide the VLA in focusing on critical actions and salient visual information, achieving effective acceleration while maintaining high accuracy. Extensive experiments show that our method achieves 1.5$\times$ lossless acceleration in LIBERO and 2.4$\times$ in SimplerEnv, with up to 6\% average performance gain. Inference frequency and latency improve by 2.2$\times$ in SimplerEnv and 1.4$\times$ in LIBERO.

Published as a conference paper at ICLR 2026
SP-VLA: A JOINT MODEL SCHEDULING AND TOKEN PRUNING APPROACH FOR VLA MODEL ACCELERA- TION
Ye Li', Yuan Meng"; M Zewen Sun't, Kangye Ji Chen Tang", Jiajun Fan", Xinzhu Ma²,
Shutao Xia?, Zhi Wang Wenwu Zhu
1 Tsinghua University MMLab, The Chinese University of Hong Kong
3 University of Illinois at Urbana-Chane Chaama-Champaign
ABSTRACT
LssionLLagguageAAction (VLA) models have attracted increasing attention for their strong control capabilities. However, their high computational cost and low execution frequency hinder their suitability for real-time tasks such as robotic manipulation and autonomous navigation. Existing VLA acceleration methods primarily focus on structural optimization, overlooking the fact that these models operate sequential decision-makiin making environments. As result, temporal redun- dancy in sequential action generation and spatial redundancy in visual input remain unaddressed. To this end, we propose SP-VLA, unified framework that acceler- ates VLA models by jointly scheduling models and pruning tokens. Specifically, we design an action-aware model scheduling mechanism that reduces temporal redundancy by dynamically switching between VLA model and a lightweight generator. Inspired by the human motion pattern of focusing on key decision points while relying intuition for other actions, categorize VLA actions into delib- erative and intuitive, assigning the former to the VLA model and the latter to the lightweight generator, enabling frequency-adaptive execution through collaborative model scheduling. To address spatial redundancy, we further develop a spatio- semantic dual-aware token pruning method. Tokens are classified into spatial and semantic types and pruned based on their dual-aware importance to accelerate VLA inference. These mechanisms work jointly guide the VLA in focusing on critical actions and salient visual information, achieving effective acceleration while maintaining high accuracy. Extensive experiments show that our method achieves 1.5x lossless acceleration in LIBERO and 2.4x in SimplerEnv, with up to 6% average performance gain. Inference frequency and latency improve by 2.2x in SimplerEnv and 1.4x in LIBERO. Moreover, on real-robot evaluations, our ap- proach maintains accuracy with only a I % drop while delivering 2.5x end-to-end acceleration. The code is available
http://giiubb.com/Chhlllllaaa
INTRODUCTION
Vision-Language-Actiinn (VLA) models integrate visual perception and language understanding to generate actionable outputs for robotic control and task execution in embodied agents, demonstrating remarkable progress across a wide range tasks (Zhang et al., 2024; Han et al., 2024; Shi et al., 2025; Figure AI, 2024; Team et al., 2024; Liu al., 2024a;b). However, VLA models are generally large-scale. For example, Google's recently released RT-X series (Brohan et al., 2023; Belkhale al., 2024; O'Neill et al.. 2024b) contains more than 55 billion parameters, and even lightweight models widely used like Open VLA (Kim et al., 2024) still exceed 7 billion parameters. The resulting computational burden leads to slow inference, making them unsuitable for real-time scenarios such as industrial control, autonomous navigation, and medical robotics.
*Corresponding Authors: yuanmeng @isinguua.cdnuu.nn wangzhi @w.tiinguu..duu.cnn research intern Tsinghua University.
Work done as

---

Published as a conference paper at ICLR 2026
Existing Vision Language Action Model
ViT Encoder
Ax A0 AGrip
Large Language Model e.g. Llama 2 7B
Action Head
Prompt
Tokenizer
Feedback
SP-VLA: Joint Model Scheduling and Token Pruning
Encoder
LLM (e.g. 7B)
Deliberative
action
Compressor
Action Head
Space Takens Semantic Tokens
Input Images
Scheduler
Lightweight Action Generator (<TK)
Environment
Intuitive action
Action Buffer
History Action
Feedback
Figure The main idea SP-VLA. Unlike traditional VLA models, SP-VLA first determines the type of the current action. ! For intuitive actions, a lightweight action generator is employed to approximate the output, while for deliberative actions, the high-precision VLA model is used to ensure accuracy. 2 When the VLA model is invoked, we further accelerate inference by adaptively pruning tokens based on integrated spatial and semantic information. By jointly leveraging the above two strategies, SP-VLA effectively directs the model's attention to critical actions and salient visual information, achieving substantial speedup without compromising accuracy. Among these components, the action head takes different forms across VLA architectures, such D-tokenizer in OpenVLA or a diffusion- based policy in CogACT.
Existing methods for VLA model acceleration focus solely on reducing the single-step computation redundancy via model compression techniques (e.g., pruning (Li et al., 2025b; Kong al., 2025), quantization (Tang et al., 2024; 2022a;b), speculative decoding (Li et al., 2025a), caching (Li et al., 2024b; Wimbauer et al., 2024; Ma et al., 2024)). Specifically, DeeR- VLA (Yue et al., 2024) introduces an early-exit mechanism that reduces the computational burden of the LLM backbone. QAIL (Park al., 2024) incorporates an imitation learning mechanism quantize the VLA model to 4-bit precision, reducing computational cost while preserving model accuracy. VLA-Cache (Xu et al., 2025) reduces computation by selectively reusing tokens deemed less informative. Although these works achieve certain levels of speedup, they primarily focus on accelerating Vision- Model (VLM) architectures, overlooking the unique characteristics of VLA models, which introduce an additional temporal dimension by generating actions step-by-step through continuous interaction with the environment. As result, accelerating VLA models presents two major challenges: (I) Given the temporal dependencies in embodied tasks, how can effectively leverage historical information to support current decision- decision-making and reduce computational redundancy? ? (2) Given the high redundancy in visual input from the camera, how can effectively retain informative visual content and reduce redundancy along the spatial dimension? To this end, we study the problem of handling temporal and spatial redundancy of VLA models for the first time, and present SP-VLA, unified framework that jointly Scheduling the model and Pruning tokens to accelerate VLA models.
To solve temporal dependencies, first design a action-type aware model scheduling approach that enables aadapivey inference by dynamically switching between the large-scale VLA model and lightweight model at different time steps. By rethinking human motion patterns, we observe that deliberate thinking typically occurs only at critical moments such as grasping or turning, while movements between those key points are executed intuitively (Schwartz, 2016; Merel al., 2019; Murray & Escola, 2020). This allows humans to perform complex tasks both quickly and accurately. Interestingly, we find that VLA models follow similar behavioral pattern like human, with actions falling into two categories: deliberative and intuitive. Based this observation, Based on this, we propose using the VLA model to generate deliberative actions, while delegating intuitive actions to a lightweight model. Specifically, assume that high- speed movements typically intuitive, whereas lowespeed movements are more likely to be deliberative, and we determine the action type each time step based historical information. To model intuitive actions, we exploit
2

---

Published as a conference paper at ICLR 2026
the inertia in object motion and design lightweight action generator based on Ridge Regression, enhanced with an action cache for efficient prediction. However, since embodied tasks are inherently more complex than simple linear movements, the generation of intuitive actions still benefits from the VLA model's directional guidance to ensure task fidelity. As a result, intuitive actions are actually produced through high-frequency switching between the lightweight generator and the VLA model.
To mitigate spatial redundancy, we design spatio-semantic dual-aware token pruning to preserve the most relevant visual information. Unlike VLMs, VLA models must understand the relative positions of objects to complete tasks, implying the need for spatial perception. Experimental results show that disrupting token arrangement or aggressively pruning background tokens, such as object contours, leads to significant performance degradation in VLA tasks. This indicates that the spatial perception of VIA models relies on the relative order of tokens and object contour information. To address this, we integrate edge and semantic information by extracting object contours using the Canny operator and estimating semantic importance via accumulated attention scores. We also dynamically adjust the token pruning threshold based on current motion speed to maximize inference efficiency.
Overall, we first perform adaptive scheduling between the VLA model and a lightweight action generator based on the action type at each time step. When invoking the VLA model, we further apply speed-award aware token pruning, enabling task-aware and frequency-adaptie effectively eliminate both temporal and spatial redundancies, guiding
inference. Through
this joint optimization, the model to focus on critical actions and salient visual elements to maximize inference efficiency. Extensive experiments demonstrate that method achieves 1.5x lossless speedup in LIBERO and 2.4x in SimplerEnv, with up to 6% performance gain, while improving inference frequency and latency by 1.4x and 2.2x, respectively. On real-robot evaluations, our approach maintains accuracy with only 1 %% drop while delivering a 2.5x end-to-end acceleration, highlighting its strong efficiency and robustness.
The main contributions of SP-VLA are as follows:
(1) To the best of our knowledge, this is the first work to accelerate VLA models via reducing the temporal and spatial redundancy. We propose a scheduling and token pruning, guiding the VLA model to focus on key actions and visual elements to achieve maximal acceleration.
framework that jointly performs model
(2) We propose an action-award model scheduling algorithm that effectively reduces temporal redundancy in VLA models. We observe that the VLA action sequences resemble human behavior, comprising intuitive and deliberative actions. Leveraging this structure, we assign intuitive actions to lightweight model and deliberative ones to the VLA model, enabling lossless, rrequency-adaptive acceleration.
(3) We propose spatio-semantic dual-aware token compression method. We find that the spatial perception of VLA models relies on the relative positions of tokens and object contour information. Based on this, we design a token pruning method that incorporates both edge features and semantic importance, effectively reducing spatial redundancy.
2 RELATED WORK
Sgioo-Languaae-Actioon Models. As LLMs gain stronger reasoning ability, the VLA paradigm emerges to extend VLMs embodied control. DeepMind's RT series, including RT-1 (Brohan et al., 2022), RT-2 (Brohan et al., 2023), RT-X (O'Neill et al., 2024b), and RT-H (Belkhale et al., 2024), are among the earliest large-scale VLA models. Additionally, the release of the Open X-Embodiment (O (O'Neill et al., 2024a) dataset has laid a strong foundation for continued research. Built on it, VLA (Kim et al., 2024) leverages the reasoning capabilities of LLaMA 2, achieving significant improvements in accuracy. On the other hand, generative models such as diffusion models have been adopted in embodied tasks to enhance the temporal coherence of actions. To (Black et al., 2024) and To.5 (Intelligence et al., 2025) incorporate Flow Matching models as action decoders, allowing VLA models to generate entire action sequences in a single pass, significantly improving execution smoothness and efficiency. Nevertheless, compared to traditional control methods (Li et al., 2023; Liu et al., 2024c; Lin et al., 2022; Li et al., 2021). the lack of attention to action efficiency hampers their deployment in more demanding applications, such as industrial assembly.
3

---

Published as a conference paper at ICLR 2026
Rondom Token Pruning
Targeting
Grasping
Moving
Putting
Velocity
1.
Reordering
Velocity
Semantic-aware Token Pruning
1.0
Velocity
Spatio-Semance Dual-awore Token Pruning
0.0
Speed Change
00
100
140
A
8.1
82
Timestep
(a)
(b)
Figure 2: The visualization of VLA model behavior. (a) shows the velocity profile of the robot arm across 50 pick-and sock-and-pacce trials, following a consistent four-phase pattern: targeting, grasping. moving, and placing. The VLA model demonstrates complex behavior by adjusting orientation at key points and learning kinematic patterns such as acceleration and deceleration. These action sequences comprise both deliberative and infuitive components. (h) shows task performance under different token distributions. Random pruning degrades accuracy, highlighting the presence of token redundancy. However, relying exclusively on semantic importance, such as through reordering or semantic-awee aware pruning, causes the model to fail in completing the task. In contrast, integrating spatial and semantic information enables efficient pruning while preserving performance, as the VLA model relies on token relative positions and object contours for spatial understanding.
Acceleration for Vision-Language-AAtton Models. A lot of work has been devoted to improving the efficiency of the VLA model. QAIL (Park al., 2024) integrates quantization into the imitation learning fine-tuning process to train quantized policies that approximate expert behavior. Fast (Pertsch et al., 2025) transforms actions into the frequency domain, enabling efficient compression by analyzing their spectral characteristics. Cache methods (Xu et al., 2025; Ji et al., 2025; 2026) accelerates inference by distinguishing background tokens from task-relevant ones and caching the less critical parts. PD-VLA (Song et al., 2025) and VLA-OFT (Kim et al., 2025) modify the autoregressive action generation in VIA models by introducing parallel decoding, significantly improving generation efficiency. However, these approaches fail to account for the specific nature of embodied tasks, such as leveraging historical information and addressing visual redundancy, thus leaving significant potential for further acceleration. Our approach adaptively switches between the VLA model and a lightweight action generator based on intuitive and deliberative actions, and prunes tokens according task complexity, enabling frequency adaptive acceleration.
A JOINT MODEL SCHEDULING AND TOKEN PRUNING APPROACH FOR VLA MODEL ACCELERATION
In this section, we provide a detailed introduction to SP- SL. The framework shown in Fig. 1. Prior to processing environmental feedback, the historical action sequence is analyzed determine whether the current step requires a deliberative or intuitive action. Intuitive actions are generated using lightweight generator, while deliberative actions handled by the VLA. Furthermore, before entering the LLM backbone, input tokens are pruned based on their spatial context and semantic importance, further reducing computational overhead. The lightweight action generator will be introduced in Section 3.1, and the token pruning strategy will be detailed in Section 3.2.
3.1 ACTION TYPE-AWARE MODEL SCHEDULING
Human motor behavior relies on deliberate thinking only for complex actions, such as grasping or turning, while other simple actions are executed intuitively (Schwartz, 2016; Merel et al., 2019;
4

---

Published as a conference paper at ICLR 2026
-iigtweiihtt Generator [Ax,60] [AGrtp]-1
VLA Model
Spatio-Semantic duar- Token Pruning
Action Head
LLM g. 7B
Legality Check
Token Pruning
Speed-aware
Space-aware Token Selection
prune rate
Ridge Regression
Encoder
Intuitive Action
Deliberative Action
Accunulated Score Conny Edge
Toben Ronking
Speed Range
Intuitive Race
x
Action Buffer
Scheduler
Figure 3: The framework of SP-VLA. SP-VLA accelerates the inference process through joint model scheduling and token pruning. Left: At each time step t, the scheduler classifies the current action intuitive or deliberative based on the historial trajectories in the action buffer. For intuitive actions, Ridge Regression estimates the translational and rotational components, reusing the gripper state at - 1. Otherwise, the VLA model will generate fine-grained action. Right: To support spatial understanding, we rank token importance by combining spatial information from the Canny operator with semantic importance, and perform
pruning for optimal acceleration.
Murray & Escola, 2020). This hybrid strategy achieves high efficiency and low energy consumption without sacrificing effectiveness. However, existing VLA models treat all actions as equally important, relying on large model (e.g.. parameter > 7B ) to generate each action through complex reasoning. In reality, coherent action sequences involve not only high-level logical reasoning but also low- level physical dynamics, including inertia and linear acceleration or deceleration during point-to- point movements, which poses significant challenge for VLA modeling. Ignoring the distinction between action types leads to substantial redundant computation and ultimately compromises motion smoothness. Therefore, leveraging this property reduce the computational burden of VLA models a pressing challenge that needs to be addressed.
Action Type Indicator. In order to identify intuitive actions in VLA -LA-generated trajectories, analyzed the behavioral patterns of VLA models and uncovered consistent patterns in grasping tasks. The robotic typically performs slow alignment with the target, then approaches the target position at high speed, and finally executes the grasping action at a moderately low speed. similar motion pattern is observed during the placement phase, and the observed behavior is illustrated in Fig. 2a. As shown in the figure, the VLA model not only learns logical reasoning capabilities but also captures dynamic patterns such as acceleration and deceleration. Therefore, we conclude that deliberative actions are required for precise operations such as turning and grasping, whereas intuitive actions are more appropriate during nigh-speet transitions between task phases. In this paper, we treat the action output of the VLA model displacement per time step, i.e., velocity. Let ar, = (az; Gy a represent the translational velocity components of the end-effector time step action ai E {a E R R'I|a,] > Umin Vi E (x, y, v, } is classified as an intuitive action all components exceed predefined threshold Umin, otherwise, it will be considered as deliberative action.
Model Schedular. Based on the above conclusion, we determine whether to use the lightweight model based on motion speed and the action cache, as shown in Fig. 3. Low velocities typically indicate fine manipulation, whereas high velocities increase the risk of significant errors when relying on the lightweight model. If a;-1 E {a E IR' ['min < lasl < Umax: Vi E {x, y. } the lightweight model can be called, Umia and Vmaa denote the velocity thresholds. On the other hand, also monitor the number of VLA-generated actions No in the action buffer SA and allow the lightweight model to be used when Ta/NA> > where NA is the total action number of SA. is predefined
5

---

Published as a conference paper at ICLR 2026
threshold. Overall, the triggering conditions for the lightweight model are:
NG
LWM 1, if a [Vmins Umax] and
NA
0, otherwise
By performing small-step. figh-freuuency model switching, we can achieve faster inference while maintaining the accuracy of action direction.
Lightweight Action Generator. To support fast and reliable action approximation, we develop lightweight generator using Ridge Regression and an action buffer to efficiently estimate upcoming actions. Although the end-effector trajectory of the manipulator is complex, we assume that short segments of intuitive actions can be approximated between time and velocity in the action buffer, the current action a, can be predicted. Specifically, the action buffer S { n ... a is used to store actions generated over the most recent 7/ Steps, is the current timesteps, a, = {{1g, 02 ... at) } the (-dimensional action vector at 1 is the timestep vector. The formulation of the Ridge Regression model is Y= =,8 + E, where = [T. , 1] E II' R=*2 is the imput, 3 E R²x denotes the parameter matrix to be fitted, is the error term, Y E R is the action buffer. To generate each actions, the model is re-fitted from scratch, with the following loss function:
linear. Therefore, by modeling the relationship
t. T = 00, 1..........
J( |X3 Y1I + \||@||
where || is the Tikhonov regularization term, which imposes an L2 penalty on the parameters, is the regularization term. The analytical solution to this equation is given by:
[ -
(3)
where I E 2x2 is the identity matrix. Once the optimal parameters of the current segment 3 is obtained, the action at the current time step can be calculated as follows:
a x,3", where x, = |t
is worth noting that, since the end-effector state this work is represented as a binary variable, we do not apply the above fitting strategy. Instead, directly reuse the value from the previous time step f 1, and delegate state transitions the end-effector to the VLA model. Finally, the predicted action is directly executed after passing a validity check.
3.2 SPANIAL-SEMANTIC DUAL-AWARE TOKEN PRUNING
To further reduce computation, we adopt a cltt-ccentiic perspective and dynamically prune less important tokens during VLA invocation, enabling the model to concentrate its attention task relevant content. Since the LLM accounts for the majority of computational overhead in VLA models, we perform token pruning before feeding tokens into the LLM, ensuring compatibility with diverse VLA architectures, Notably, we observe that VLA models are highly sensitive to both the relative positions of input tokens and object contour-related tokens, as evidenced by the experimental results in Fig. 2b. As illustrated, randomly dropping tokens reduces accuracy but does not prevent task completion, suggesting that many tokens are redundant. is worth noting that even without pruning, reordering tokens solely according to their semantic importance results in task failure, underscoring the importance of token relative positioning for spatial understanding in VLA models. Moreover, even without altering the relative positions of tokens, pruning solely based semantic importance can remove critical background information, also leading to task failure. Finally, reintroducing positional tokens restores model performance, underscoring the critical role of both token relative ordering and object contour-related tokens in supporting accurate spatial localization.
Semantic-aware Token Importance. Given the input image X, the vision encoder transforms it into a sequence of tokens. We use the final layer the encoder as the basis for token selection. The queries, keys, and values can be calculated as follows:
Q XW, g: K XW*. V XW...
(5)

---

Published as a conference paper at ICLR 2026
Table I: Comparisons with the state-of-the-arts on LIBERO. To adapt these methods for the VLA model, we introduced several enhancements. Specifically, "+R" denotes the preservation of relative token positions, while "+S" represents the incorporation of Canny edge information. Compared to existing approaches, our method achieves 1.35x speedup without any performance loss. Furthermore, it enables a 1.5x acceleration with less than a 3% drop accuracy.
Success Rate (%, / Speed (t)
Method
Goal
Object
Spatial
Long
Average
FLOPSs (%,, .)
VLenVLA
75.40 / 1.00
86.20 1.00
83.80 / 1.00
53.00 / 1.00
74.60 1.00
100
VL.
74,20 1.33
84.00 / 1.33
83.40/ / 1.33
52.80 1.33
73.60 / 1.33
75.55
FoPru
59.80 / 1.29
81.20 1.29
71.60 1.30
26.20 1.35
59.70 1.31
77.20
PruMerge
0.00 1.54
0.00 / 1.32
0.00 1.27
0.00 1.32
0.00/ 1.36
73.63
FastVLM R S
73.20 / 1.21
77.00 1..00/1.11 1.11
79.80 / 1.12
36.60 1.20
66.65 / 1.16
86.22
VisionZáp + R S
46.00 /1.20
47.40 1,23
34.20 / 1.19
4.60 1.22
33,05 / 1,21
81.95
Ours (Speed)
73.60 1.66
82.40 1.44
80.00 / 1,47
51.60 1.42
71.90 1.50
66.51
Ours (Acc.)
75.40 / 1.46
85.60 1.30
84.40 / 1.30
54.20 1.32
74.90 1.35
73.64
where Q, K, V E Nxde Wg. W. and W, E RRdxd are trainable weight matrices, and N the sequence length. The cumulative importance score of the tokens is given by:
QK
Attn = Softmax
V,
AccuAttn N (e & IM) (ttn):
(6)
Vd
where Attn E R Nx denotes the attention weight matrix, vec(Attn) E N2x is the column- -wise vectorization, e xg an all-one vector, and & denotes the Kronecker product. Based on this, we first identify semantically relevant tokens The by selecting those with cumulative attention scores exceeding a threshold tg,, i.c., Tse = {x, AccuAttn; {x.)-
Spatial-aware Token Importance. We hypothesize that spatial information is primarily encoded in object contours. Therefore, we extract spatially informative tokens using the Canny edge detector. X, CX) denotes the edge-only image that preserves only contour information extracted from X. We then obtain an ordered sequence of edge-based tokens using Top = fr(X,), where fF(()) denotes the token extraction function.
Finally, the selected token set is obtained by computing the order-preserving union of the two, T seelect UT ap)), where U--) denotes a union operation that preserves the original token ordering.
To align with the model collaboration strategy, disable token pruning under low-speed conditions to avoid disrupting precise manipulations. Furthermore, motivated by the observation that higher motion speeds generally correspond to more intuitive actions, we define the pruning ratio to be positively correlated with the current velocity. Accordingly, the retained token ratio is defined as:
1 Upmin
Tr(o)=
2 Pmnin
Upmin
Upmax Pann
where pamn is the minimum velocity threshold, Upmax is the maximum velocity of VLA model.
4 EXPERIMENTAL RESULTS
In this section, we present extensive experimental results to demonstrate the superior performance of SP-VLA. LIBERO consists of four task suites (Spatial, Object, Goal, and Long), covering 130 tasks with 2000 trajectories to evaluate model robustness. SimplerEnv provides three settings (Google- VM, Google VA, and Bridge-VM. with variations in color, material, lighting, and camera pose for robustness assessment. We conduct real-world experiments by deploying SP-VLA on Franka Research 3 robot to validate its practical effectiveness. In these experiments, we use CogACT as the base model and fine-tune it with 150 trajectories per task collected using the GELLO suite. Experiments are run on NVIDIA 1000 GPUs (40GB), and all reported results are averaged over three independent runs with different random seeds ensure statistical robustness.
7

---

Published as a conference paper at ICLR 2026
Table The acceleration effects of individual modules. As shown in the table, model scheduling yields the most significant acceleration for the VLA model with the least accuracy loss. In contrast, the model exhibits higher sensitivity token pruning, and its performance collapses entirely when
object edge information is removed.
Success Rate (%, 1) Speed up (1)
Method
Average
FLOPs (%, !)
Goal
Object
Spatial
Long
Ours
75.40 1.46
85.60 / 1.30
84.40 / 1.30
54.20 / 1.32
74.90 / 1.35
73.64
w/o Pruning
74.40 1.23
84,20 1.27
84.00 / 1.18
53.30 1.39
73.98 1.27
78.75
w/o Scheduling
77.31 1.24
81.80 1.16
79.00 1.30
48.00 1.13
71.52 1.21
82.55
w/o Canny
33.60
1.34
39.00 1.35
22.00/ 1.35
1.10/ 1137
23.93
1.35
73.40
Table 3: Comparisons with the state-of-the-arts on SimplerEnv. We apply SP-VLA the LLM of CogACT, where the meanings of "+R" and '+S' follow Table 1. As shown, SP-V SP-VLAachieves SOTA performance across diverse tasks, delivering significant speedup while also improving accuracy.
Success Rate (%, 1) /Seed up (t)
SIMPLER
Method
PickCan
MoveNear
Drawer
DrawerApple
Average
FLOPS (%.
CogACT
91.30 1.00
85.00/ 1.00
71.80 1,00
50.90/ 1,00
74.80 1.00
100.00
Random Dropping
9.70 1.20
20.40/ 1.20
53.50 0000/111 1,20
0.00/ 1.20
20.90/ 1120
58.50
Visual Matching
FastV
92.60 1.21
81.40/
69.80
$2.40/ 1.21
74.10
1.21
42.00
VLA -CChhe
92.00 1.38
83.30/ 1.38
70,50/ 1.38
$1.60/ 1.38
74.40 1.38
80.10
VfficientVI.A
43.30
81.30/ 1.93
1,93
1.93
28.90
Ours
90.00 / 2.62
82.08 2.52
75.35 / 1.80
52.78/ 1.67
75.05 / 2.15
38.15
CogACT
89.60 1.00
80.80 1.00
28.30 1.00
46.60 / 1.00
61.30 1.00
100.00
Random Dropping
4.00/1.20 1.20
16.10/ 1.20
15.60 1.20
0.00 1.20
8.90 1,20
58.50
Visual
FastV
91.40 1.19
78.60 1.19
27.60
1.19
50.60/ 1.19
62.10 1.19
42.00
Aggregation
VLA Cachh
91.70 1.37
79.30 1..77
32.50 1.37
45. 80 1.37
62.30 1.37
EfiiientVLA
93.20 1.91
75.80 1.91
26.90/ 1,91
49.20 1.91
61.20 1.91
28.90
Ours
86.18 2.48
77.33 2.63
55.29 1.81
41.80 1.44
65.16 2,09
40.20
Success Rate (%, 1) Speed up (t)
SIMPLER
Method
PutSpoon
PutCarrot
StackBlock
PutEggplant
Average
FLOPs
CogACT
/1.00
50.80/ 1.00
15.00 1.00
67.50/ 1,00
51.30/ 1.00
100.00
Random Dropping
52.17 1.20
39.13/ 1.20
8.69 1.20
26.08 / 1.20
29.70 1.20
85.00
WindowX
FoPru+
52.17 1.33
39.13/1.33
13.04 1.33
69.56 1.33
43.38
78.00
FastVLM R
34.78 1.14
30.43/ 1.14
4.35 1.14
30.43 1.08
25.00/ /.13
90.50
Ours
70.83 /3664
54.17 1.73
29.17 /2.54
75.00 1.72
57.29
2.41
35.35
Parameter Settings. In this experiment, set the buffer size 6 and the deliberation ratio to t = 0.5. The choice of velocity thresholds is device dependent. In practice, one should first determine the maximum and minimum task-execution speeds of the embodied system, and then use 1/4 and 3/4 of this range as the values for Vmin and Vmax. respectively. For example, in simulation, we assign set of parameters for SP-VLA to broad class of tasks, since different task types may require different configurations. In the real-world experiments of this paper, however, single parameter set sufficient for the Franka robot.
4.1 SIMULATION RESULTS
Table and 3 report the main results of SP-VLA. SP-VLA achieves the best results in LIBERO, consistently delivering a 1.35x speedup without accuracy loss, and up to 1.5x faster inference with a slight 3% accuracy drop. In SimplerEnv tasks, it further achieves a 2x speedup with improved performance, indicating a degree of error-correction capability. Notably, on the Visual Aggrega- tion Drawer task, SP-VLA improves performance by about 27% while achieving 1.8x speedup, demonstrating clear error-correction capability. Besides, the VLA model is highly sensitive to spatial information: disrupting token order or retaining too few tokens severely impairs perception, often leading to task failure, particularly with generic compression methods. To address this, we reorder tokens and add position tokens ('+R' and '+S'), but these approaches still perform poorly with substantial performance degradation. Even after supplementing positional information and preserving token order, FoPru (Jiang et al., 2024) and VisionZip (Yang et al., 2024) still suffer from substantial
8

---

Published as a conference paper at ICLR 2026
Table 4: Real-robot performance on Franka. We evaluate two manipulation tasks over 50 trials each (20 morning, 10 noon, 20 evening). SP-VLA achieves over 2.5x acceleration on average while preserving accuracy, reduces FLOPs by over 60 %,, and lowers inference latency by nearly 50%.
Success Rate (%, 1) Speed Up (1)
Method
Pick Up
Pick and Place
Average
FLOPs (%, 4)
Latency (s, !)
CogACT
80.00 1.00
74.00 / 1.00
77.00 1.00
100
0.27
SP-VLA
78.00 / 2.46
74.00 / 2.57 76.00/ / 2.52
35,55
0.13
Table Latency and Frequency in the SimplerEnv Environment. We measure these metries on an RTX 4090. SP-VLA achieves approximately 2.2x inference speedup, highlighting its capability for aware dynamic acceleration that adapts frequency different stages of the task.
Frequency (Hz 1) Latency (s, 4)
Evaluation
SIMPLER
Indicator
PickCan
MoveNear
Drawer
DrawerApple
Average
CogACT
4.00 / 0.25
4.00 /.225
0.25
3.85 /0.26
3.23/ 0.331
3.77 /
Visual Matching
SP-VIA
9.09/ /0111
8.33 / 0.12
0.14
7.69 7..13
8.06/0.13
Frequency (Hz, 7)/ Latency (s,
Evaluation
SIMPLER
Indicator
PutSpoon
PutCarrot
StackBlock
PutEggplant
Average
CogACT
/.00/ 12.5/ 0.08
4.00 0.25
4.00 / 0.25
3,85
0.25
WindowX
SP-VLA
6.25 / 0.16
8.33 / 0.12
7.69 / 0.13
8.69 0.12
performance degradation with limited speedup. These results demonstrate that SP-VLA effectively identifies both temporal and spatial redundancies in VLA models, and accelerates the inference process through joint model scheduling and token pruning, while preserving the model's spatial perception capabilities and overall performance. This confirms the significant effectiveness of our approach in accelerating VL. inference.
4.2 REAL-WORLD RESULTS
To further validate the acceleration gains of SP-VLA, we first train CogACT using 150 trajectories collected for each the two tasks-Pick up the cylinder and Pick and Place the cylinder. We then deploy SP-VLA are set using 1/4 and 3/4 of Franka's velocity range as Vmmn and Vmaz. respectively, with T = 0.5 and 6. For each task, conduct 50 evaluations (20 in the morning. 10 at noon, and 20 in the evening) and report the average performance to approximate operation across different time periods. As shown in Table CogACT achieves success rates of 80% and 74% the two tasks. After integrating SP-VLA, the success rates become 78% and 74%. This reflects only a 1% decrease in average accuracy while achieving 2.52x acceleration. These results demonstrate that SP-VLA provides stable and consistent acceleration in both simulation and real-world deployment.
real Franka Research 3 robot to evaluate its performance. The hyperparameters
4.3 ANALYSIS
The Acceleration Effects of Individual Modules. The ablation results summarized in Table 2. Specifically, we conduct three ablation experiments here: (1) disabling token pruning, (2) removing model scheduling, and (3) excluding positional information from the token pruning process. Overall, model scheduling emerges as the most effective component for accelerating the VLA model, achieving a 1.27x speedup with only 1% drop accuracy. This indicates that VLA models contain substantial temporal redundancy. In contrast, token pruning yields only moderate acceleration but results in substantial accuracy drop, indicating that the VLA model highly sensitive to token reduction and offers limited redundancy for compression. Notably, eliminating the Canny edge information results in dramatic 50% degradation performance, effectively collapsing the model's functionality. This underscores the critical role of relative token positions and object contour information in enabling the VLA model's 's spatial perception. Ultimately, the joint application of these techniques yields the best overall acceleration, with compromise in model accuracy.

---

Published as a conference paper at ICLR 2026
LIBERO-Spate SSptiall:: : Pick up black bowl on ramekinn and place plate.
LIBERO-Long Pul the black bowl the bottom drawer of the cabinet and close
LIBERO-Goal Put the wine bottle top of the cabinet.
LIBERO-Object
Pick the bbq sauce and place it in the basket.
Figure 4: Visualizations of SP-VLA across different tasks. As shown in the figure, our method efficiently prunes redundant image regions to accelerate VLA inference while preserving key object contours maintain spatial perception.
Task prompt Pick up the cylinder and place it into the basket.
Task Prompt: Pick up the cylinder.
Figure Visualizations of SP-VLA on real tasks. As illustrated in the figure, our method selectively prunes unnecessary tokens while retaining critical visual cues, enabling efficient acceleration in real- world scenarios without sacrificing task-relevant information.
Frequency and Latency. We evaluate the inference frequency and latency of SP-VLA on single NVIDIA RTX 4090 (40GB), with results in Table 5. CogACT runs at less than 4 Hz, while our method increases the frequency over Hz 2.2x improvement -lannn with 2.2x reduction in per-inference latency. Furthermore, on LIBERO, our method delivers a 1.4x frequency improvement, indicating that VL.A models exhibit substantial temporal and spatial redundancy, which our approach effectively exploits for dynamic frequency acceleration. More detailed results are in Appendix A.2.
Visualizations. To evaluate the efficiency of SP-VLA, we present both simulation and real-world visualizations Fig. and Fig. 5, illustrating how the method accelerates VLA inference while preserving task-relevant information. Fig. 4 shows how SP-VLA performs task completion across various tasks during the invocation of the model. As shown in the figure, SP-VIL BL- adaptively identifies redundant components and prunes tokens accordingly to accelerate inference. Morever, it effectively preserves object edge information, maintaining the spatial perception capability required by the model. These results demonstrate the effectiveness of our spatio-semantic dual-aware token pruning method in significantly enhancing inference efficiency. Fig. illustrates the real-world performance of SP-VLA. As shown in the figure, our method effectively identifies and removes redundant tokens while preserving the essential content, enabling efficient acceleration without compromising task- relevant information. This demonstrates that SP-VLA preserves the execution accuracy of the base model while simultaneously achieving substantial acceleration. For details on parameter sensitivity analysis, in dedephh experimental analysis, the acceleration ratios of different model parts and additional experiments and visualizations, please refer to Appendices A.3, A.4, A.5, A.7, A.8 and A.9.
CONCLUSION
In this work, we propose SP-VLA, a unified framework that accelerates VLA models through joint model scheduling and token pruning. By dynamically switching between a full VLA model and a lightweight generator based on deliberative intuitive actions, and pruning tokens according to spatio-semantic importance during VLA invocation, SP-VLA enables frequency- awwree and task- adaptive acceleration. Extensive experiments demonstrate that method achieves 1.5x lossless speedup in LIBERO and 2.4x in SimplerEnv, with up to 6% performance gain, while simultaneously improving both inference frequency and lateney by 1.4x in LIBERO and 2.2x in SimplerEnv.
10

---

Published as a conference paper at ICLR 2026
ETHICS STATEMENT
This work focuses solely on accelerating embodied models and involves only robotic arm simulations and real-world experiments. does not involve human subjects, sensitive personal data, or ethically concerning applications. The research does not present foreseeable risks of misuse or harm, nor does it raise issues of bias, discrimination, privacy, or security. All experiments were conducted in compliance with standard research integrity practices.
REPRODUCIBILITY STATEMENT
We have taken several measures ensure the reproducibility our results. All experiments were conducted with multiple random seeds and repeated runs to confirm robustness. The backbone models and checkpoints used in this work are publicly available and open-source. The experimental envi- ronments (LIBERO and SimplerEnv) are standardized and publicly accessible. All hyperparameters and implementation details will be fully documented and released in a public GitHub repository. and additional sensitivity analyses and extended results provided in the appendix to further support reproducibility.
8 ACKNOWLEDGMENTS
This work supported by National Natural Science Foundation of China (Grant No. 92467204, 62472249 and 62402264), and Shenzhen Science and Technology Program (Grant No. JCY1202208181010140000 and KJZD200249963022000011
REFERENCES
Suneel Belkhale, Tianli Ding, Ted Xiao, Pierre Sermanet, Quon Vuong, Jonathan Tompson, Yevgen Chebotar, Debidatta Dwibedi, and Dorsa Sadigh. Rt-h: Action hierarchies using language. arXiv preprint arXiv:2403.0/822 2024.
Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al. Ne: A vision-language-actioo flow model for general robot control. arXiv preprint arXiv:2410.2416. 2024.
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, et al. Rt-1: Robotics transformer for real-world control at scale. arXiv preprint arXiv:2212.068) 2022.
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al. Rt-2: Vision Vision-language-action models transfer web knowledge to robotic control. arXiv preprint arXiv:2307.158/6 2023.
Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai, Junyang Lin, Chang Zhou, and Baobao Chang. An image is worth 1/2 tokens after layer 2: Blug-and-play inference acceleration for large vision- language models. In European Conference on Computer Vision, pp. 19-35. Springer, 2024.
Figure AI. Helix: A vision-languagg--ction model for generalist humanoid control. // www. fiqure. ai/news helix, 2024.
https:
ByungOk Han, Jaehong Kim, and Jinhyeok Jang. A dual process vla: Efficient robotic manipulation http://..rr/bbb///////e
leveraging vlm, 2024. URL
15549.
Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, et al. To.st A vision-lagguage-actinn model with wornd generalization. arXiv preprint arXiv:2504.16055 2025.
Kangye Ji, Yuan Meng, Hanyun Cui, Ye Li, Shengjia Hua, Lei Chen, and Zhi Wang. Block-wise adaptive caching for accelerating diffusion policy. arXiv preprint arXiv:2506. 13456, 2025.
11

---

Published as a conference paper at ICLR 2026
Kangye Ji, Yuan Meng, Zhou Jianbo, Ye Li, Hanyun Cui, and Zhi Wang. Sparse actiongen: Acceler- ating diffusion policy with real-time pruning. arXiv preprint arXiv:2601.128%. 2026.
Lei Jiang, Weizhe Huang, Tongxuan Liu, Yuting Zeng, Jing Li, Lechao Cheng, and Xiaohua Xu. Fopru: Focal pruning for efficient large vision- -ainual models. arXiv preprint arXiv:2411.14100 2024.
Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al. Openvla: An open-source Assionlanguaagcactinnn model. arXiv preprint arXiv:2406. 09246, 2024.
Moo Jin Kim, Chelsea Finn, and Percy Liang. Fine-tuning vision laaiin-lannuge-acttion models: Optimizing speed and success. arXiv preprint arXiv:2502. 19645, 2025.
Zhenglun Kong, Yize Li, Fanhu Zeng. Lei Xin, Shvat Messica, Xue Lin, Pu Zhao, Manolis Kellis, Hao Tang, and Marinka Zitnik. Token reduction should go beyond efficiency in generative models-froo vision, language to multimodality. arXiv preprint arXiv:2505. 8227,, 2025.
Xuanlin Li, Kyle Hsu, Jiayuan Gu, Karl Pertsch, Oier Mees, Homer Rich Walke, Chuyuan Fu, Ishikaa Lunawat, Isabel Sich, Sean Kirmani, Sergey Levine, Jiajun Wu, Chelsea Finn, Hao Su, Quan Vuong. and Ted Xiao. Evaluating real-world robot manipulation policies simulation. arXiv preprint ar arXiv:2405.055 059411 2024a.
Ye Li, Fuyong Wang, Zhongxin Liu, and Zengqiang Chen. Leader-follower optimal consensus of discrete- time linear multi-agent systems based q-learning. In Proceedings of 2021 Chinese Intelligent Systems Conference: Volume I, pp. 492-501. Springer, 2021.
Ye Li, ZhongXin Liu, Ge Lan, Malika Sader, and ZengQiang Chen. A ddpg-based solution for optimal consensus of continuous- time linear multi-agent systems. Science China Technological Sciences, 66(8):2441-2455 2023.
Ye Li, Jiahe Feng. Yuan Meng, Kangye Ji, Chen Tang, Xinwan Wen, Shutao Xia, Zhi Wang, and Wenwu Zhu. Ts-dp: Reinforcement speculative decoding for temporal adaptive diffusion policy acceleration. arXiv preprint arXiv:2512. 15773, 2025a.
Ye Li, Chen Tang, Yuan Meng, Jiajun Fan, Zenghao Chai, Xinzhu Ma, Zhi Wang, and Wenwu Zhu. Prance: Joint token- iiken-optimization and structural channel-pruning for adaptive vit inference. IEEE Transactions Pattern Analysis and Machine Intelligence, 2025b.
Yingxin Li, Ye Li, Yuan Meng, Xinzhu Ma, Zihan Geng, Shutao Xia, and Zhi Wang. Ems: Adaptive evict-then merge strategy for head-wise kv cache compression based on global-local importance. arXiv preprint arXiv:2412.0885 2024b.
Runji Lin, Ye Li, Xidong Feng, Zhaowei Zhang, Xian Hong Wu Fung, Haifeng Zhang, Jun Wang, Yali Du, and Yaodong Yang. Contextual transformer for offline meta reinforcement learning. arXiv 2022.
preprint ar arXiv:2211.088
Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning. Advances in Neural Information Processing Systems, 36:44776-44791, 2023.
Jiaming Liu, Mengzhen Liu, Zhenyu Wang, Lily Lee, Kaichen Zhou, Pengju An, Senqiao Yang, Renrui Zhang, Yandong Guo, and Shanghang Zhang. Robomamba: Multimodal state space model for efficient robot reasoning and manipulation. arXiv preprint 1:Xx::2406.043399 2024a.
Songming Liu, Lingxuan Wu, Bangguo Li, Hengkai Tan, Huayu Chen, Zhengyi Wang, Ke Xu, Hang Su, and Jun Zhu. Rdt-1b: diffusion foundation model for bimanual manipulation. arXiv preprint arXiv:2410.07866 2024b.
Zhongxin Liu, Ye Li, Ge Lan, and Zengqiang Chen. A novel data-driven model-free synchronization protocol for discrete-tie time multi-agent systems via td3 based algorithm. Knowledge-Bact Based Systems, 287:1 11430, 2024c.
12

---

Published as a conference paper at ICLR 2026
Xinyin Ma, Gongfan Fang, Michael Bi Mi, and Xinchao Wang. Learning-to-cer tochcchccc Accelerating diffusion transformer via layer caching, Advances in Neural Information Processing Systems, 37: 133282-133300 2024.
Josh Merel, Matthew Botvinick, and Greg Wayne. Hierarchical motor control mammals and machines. Nature communications, 10(1))5489, 2019.
James M Murray and G Sean Escola. Remembrance of things practiced with fast and slow learning in cortical and subcortical pathways. Nature Communications, 11(1):6441, 2020.
Abby O'Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, al. Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration O. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 6892 6903. IEEE, 2024a.
Abby O' O'Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, al. Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration O. In 2024 IEEE International Conference Robotics and Automation (ICRA), pp. 6892-6903. IEEE, 2024b.
Seongmin Park, Hyungmin Kim, Wonseok Jeon, Juyoung Yang. Byeongwook Jeon, Yoonseon Oh, and Jungwook Choi. Quantization-awar aware imitation-learning for resource-effciient robotic control. arXiv preprint arXiv:2412.01054 2024.
Karl Pertsch, Kyle Stachowicz, Brian Ichter, Danny Driess, Suraj Nair, Quan Vuong, Oier Mees, Chelsea Finn, and Sergey Levine. Fast: Efficient action tokenization for vision-language-action models. arXiv preprint arXiv:2501.09747, 2025.
Andrew B Schwartz. Movement: how the brain communicates with the world. Cell, 164(6):
1122-1135, 2016.
Yuzhang Shang, Mu Cai, Bingxin Xu, Yong Jae Lee, and Yan Yan. Llava-prumerge: Adaptive token reduction for efficient large multimodal models. arXiv preprint arXiv:2403.1 240.::2003./3888. 2024.
Lucy Xiaoyang Shi, Brian Ichter, Michael Equi, Liyiming Ke, Karl Pertsch, Quan Vuong, James Tanner, Anna Walling, Haohuan Wang, Niccolo Fusai, et al. Hi robot: Open-ended instruction following with hierarchical vision-language-actioo models. arXiv preprint arXiv:2502.19417. 2025.
Wenxuan Song, Jiayi Chen, Pengxiang Ding, Han Zhao, Wei Zhao, Zhide Zhong, Zongyuan Ge, Jun Ma, and Haoang Li. Accelerating lision-languaae-acctonn model integrated with action chunking via parallel decoding, 2025. URL /ttps://arxiv..r. org/ abs/ 02310.
Chen Tang, Kai Ouyang, Zhi Wang, Yifei Zhu, Wen Ji, Yaowei Wang, and Wenwu Zhu. Mixed- precision neural network quantization via learned layer-wise importance. In European conference on computer vision, pp. 259-275. Springer, 2022a.
Chen Tang, Haoyu Zhai, Kai Ouyang, Zhi Wang, Yifei Zhu, and Wenwu Zhu. Arbitrary bit-width network: joint layer-wise quantization and adaptive inference approach. In Proceedings of the 30th ACM International Conference on Multimedia, pp. 2899-2908, 2022b.
Chen Tang, Yuan Meng, Jiacheng Jiang, Shuzhao Xie, Rongwei Lu, Xinzhu Ma, Zhi Wang, and Wenwu Zhu. Retraining-free free model quantization via one-shot weight-coupling learning. In Proceedings the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 15855-15865, 2024.
Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, et al. Octo: An open-source generalist robot policy. arXiv preprint arXiv:2405. /22 2024.
Pavan Kumar Anasosalu Vasu, Fartash Faghri, Chun-Liang Li, Cem Koc, Nate True, Albert Antony, Gokul Santhanam, James Gabriel, Peter Grasch, Oncel Tuzel, et al. Fastvlm: Efficient vision encoding for vision language models. arXiv preprint 2r2 133033 2024.
13

---

Published as a conference paper at ICLR 2026
Felix Wimbauer, Bichen Wu, Edgar Schoenfeld, Xiaoliang Dai, Ji Hou, Zijian He, Artsiom Sanakoyeu, Peizhao Zhang, Sam Tsai, Jonas Kohler, et al. Cache me if you can: Accelerating diffusion models through block caching. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 6211-6220 2024.
Siyu Xu, Yunke Wang, Chenghao Xia, Dihao Zhu, Tao Huang, and Chang Xu. Vla-cache: Towards efficient vision-languuge-nction model via adaptive token eaching in robotic manipulation. arXiv preprint arXiv:2502.02115, 2025.
Senqiao Yang, Yukang Chen, Zhuotao Tian, Chengyao Wang, Jingyao Li, Bei Yu, and Jiaya Jia. Visionzip: Longer better but not necessary in vision language models. arXiv preprint arXiv:2412.04467. 2024.
Yantai Yang. Yuhao Wang, Zichen Wen, Luo Zhongwei, Chang Zou, Zhipeng Zhang, Chuan Wen, and Linfeng Zhang. Efficientvla: Training-free acceleration and compression for vision-language- action models. arXiv preprint arXiv:2506. 100:::500..000000 2025.
Yang Yue, Yulin Wang, Bingyi Kang, Yizeng Han, Shenzhi Wang. Shiji Song, Jiashi Feng, and Gao Huang. Deer-vla: Dynamic inference of multimodal large language models for efficient robot execution. Advances Neural Information Processing Systems, 37:56619-56643,, 2024.
Jianke Zhang, Yanjiang Guo, Xiaoyu Chen, Yen-Jen Wang, Yucheng Hu, Chengming Shi, and Jianyu Chen. Hirt: Enhancing robotic control with hierarchical robot transformers. arXiv preprint arXiv:2410.05273, 2024.
14

---

Published as a conference paper at ICLR 2026
A APPENDIX
A. THE USE OF LARGE LANGUAGE MODELS (LLMS)
In this work, Large Language Models (LLMs) were used solely to polish the language for clarity and readability. No LLMs were employed for idea generation, experimental design, data analysis, or any other part of the research process.
A.2 LATENCY AND FREQUENCY
Table 6: Latency and Frequency in the LIBERO Environment. We measure these metrics an RTX 4090. As shown in the table, SP-VIA achieves about a 1.4x inference speedup.
Frequency (Hz, f) latency (s,
Evaluation Inidcator
Goal
Object
Spatial
Long
Average
OLe SP-VLA
3.13 / 0.32
3.27 / 0.31
2.99/ / 0.33
3.32 / 0,30
3.18 / 0.32
4.34 / 0.23
0.22
4.37 / 0.23
4.44
4.43 / 0.23
Table Latency and Frequency in the SimplerEnv Environment. We measure these metrics on an RTX 4090. As shown in the table, SP-VLA achieves about 2.2x inference speedup.
Frequency (Hz, 1) latency (s,
Evaluation
SIMPLER
Indicator
PickCan
MoveNear
Drawer
DrawerApple
Average
CogACT
4.00/ 0.25
4.00/
3.85/ 0.26
3,23 0.31
3.77 0.27
Visual Matching
SP-VLA
9.09 0.11
0.12
0.14/0.14
7.69 / 0.13
8.06 0.13
CogACT
4.17
3.85 0.26
3.45 0.29
2.86 0.35
3.58 0.29
Visual Aggregation
VP-VLA
9.09/0.11
9.09/0.1
0.16
4.26/
7.13/0.15
Frequency (Hz, 1) latency (s, J)
Evaluation
SIMPLER
Indicator
PutSpoon
PutCarrot
StackBlock
PutEggplant
Average
CogACT
4.00 0.25
4.00 0.25
4.00 0.255
3.85 0.26
3.96 0.25
WindowX
SP-
12.5 0.08
6.25 0.16
8,33/0.12
7.69 0.13
8.69 00.2
Tables 6 and report the frequency and latency measurements of SP-VLA on an RTX 4090. As shown in the table, OpenVLA and CogACT achieve only 3-4 Hz inference frequency. By incorporating SP-VLA. their inference rates are improved by approximately 1.4x and 2.2x. respectively, while preserving model accuracy. This suggests that VIAA models contain substantial temporal and spatial redundancies, offering considerable acceleration potential. By effectively identifying and exploiting both, SP-VLA achieves significant speedups and demonstrates strong application prospects.
A.3 SENSITIVITY ANALYSIS OF KEY PARAMETERS.
Table 8 presents ablation study in the LIBERO simulation environment, examining the impact of execution speed V. buffer size n, and the proportion of deliberative actions т. Specifically, execution speed is varied by +25% around Vmaz and Vmin. buffer size is {4, 83. and the proportion of deliberative actions in the buffer is chosen from {3/8, 4/8, 5/8). The results show how model accuracy and speedup vary under different settings. Overall, SP-VLA robust speed changes, with +25% fluctuations having little impact on accuracy or acceleration. In contrast, buffer size and the proportion of intuitive actions T have a much stronger effect, significantly influencing accuracy. This is because, even during skipping, SP-VLA relies the VIAA model to provide correct directional guidance and critical decision points, thereby preserving overall performance.
A.4 ANALYSIS OF EXPERIMENTAL RESULTS.
Analysis for Baseline Methods. Owing the lack of direct baseline, instead compare method with of approaches that have demonstrated strong performance LLAVA. Tables and 3 show that the majority of baseline methods do not perform well VLA models.
15

---

Published as a conference paper at ICLR 2026
Table 8: Sensitivity Analysis of Key Parameters. We present the sensitivity analysis of SP-VLA in the LIBERO environment. We vary execution speed (+25% around Viaax and Vuin), buffer size (n 8), and the proportion of deliberative actions (T E 3/8, 4/8, 5/8), and report their effects on model accuracy and acceleration. The results show that SP-VLA is robust to speed changes, whereas buffer size and the proportion deliberative actions exert a stronger influence, significantly affecting accuracy.
Success Rate (%, / Speed up (1)
Task
Scaling
Vmin (0.2)
Vmaz (0.5)
7 (0.5)
n (6)
0 125%. 25%, 5/8, 8] [25%. 25%, 3/8, 4]
82.4 1.44
82.4/ 1.44
82.4 1.44
82.4 1.44
Object
82.2 1.41
83.2/ 1.33
81.7 1.34
68.4 / 1,58
83.6 1.37
81.6 1.38
64.4/ 1.75
80.8 1.33
0 [25%, 25%, 5/8, 8]
73.6 1.66
73.6/ 1.66
73.6/ 1.66
73.6 1.66
Goal
72.8 1.41
70.4 1.47
71.7/ 1.45
66.2 1.87
[25%, 25%, 3/8, 4]
74.8 1.22
69.0 1.78
1.91
74.0/ 1.31
0 [25%. 25%, 5/8, 8]
80.0 1.47
80.0 1,47 1.47
80,0 1.47
80,0 / 1.47
Spatial
77.6 1.49
80.0 / 1.47
79.1 / 1.36
74.8 1.59
[25%, 25%, 3/8, 4]
78.6 1.43
78.4 1.52
62.2 / 1.83
74.4 / 1.27
1.42 51.6 1.42
51.6 1.42
51.6/1.42
51.6/ 1.42
Long
[25%, 25%, 5/8, 8]
50.4
49.2 1.31
4
1.37
[25%. 25%. 3/8, 4]
/
46.6 1.53
42.5 1.77
44.6/ 1.32
Although the baseline methods are effective lightweight solutions for VLMs, VLA models differ in two fundamental aspects: they are highly sensitive spatial information and are required to perform sequential decision-making. Consequently, VLA models exhibit both temporal and spatial redundancies. These characteristics render token lightweighting strategies designed for VLMs unfit for VLA applications.
(1) Failed extraction of spatial information. Since FoPru (Jiang et al., 2024) and PruMerge (Shang et al., 2024) already incorporate mechanisms for extracting spatial information, did not add Canny-based edge detection to these methods. However, their spatial information extraction primarily aims preserve semantic integrity rather than focusing on the relative positioning between objects, which leads to performance degradation when applied to VLA models.
(2) Feature fusion-induced spatial ambiguity. VisionZip (Yang et al., 2024) merges tokens to compress semantic information, which proves effective for VLMs but disrupts spatial information in VLA models, leading to a collapse in performance.
(3) Performance degradation due overlooked temporal redundancy. Although FastVLM (Vasu et al., 2024) and FastV (Chen et al., 2024) performs token pruning and information augmentation, retains a fixed number of tokens each timestep. making unable adapt to the varying complexity of VI.A tasks. This limitation results in degraded performance. In contrast, SP-VLA maintains a constant cumulative attention threshold, dynamically adjusting the number of retained tokens preserving more at critical moments and fewer during less demanding mhases-thereby effectively preserving the performance of the VLA model.
In contrast, VLA-Cache (Xu et al., 2025) and EfficientVLA (Yang et al.. 2025) are acceleration algorithms specifically designed for VLA models. VLA-Cache leverages KV cache reuse, while EfficientVI.A combines layer skipping, token pruning, and activation caching to accelerate both the VLM and the Action Head, achieving relatively stronger speedups. SP-VI.A on the other hand, targets temporal and spatial redundancies without altering the model itself and is therefore, some extent, orthogonal to these two methods.
Analysis for the Simulation Environments. We evaluate SP-VLA using two simulation environ- ments: LIBERO (Liu et al., 2023) and SimplerEny (Li et al., 2024a). LIBERO consists of four task suites (Spatial, Object, Goal, and Long), covering 130 tasks with 2000 trajectories evaluate model robustness. SimplerEnv provides three settings (Google-VM) VM. Google VA, and Bridge- VM)) with variations in color, material, lighting, and camera pose for robustness assessment. LIBERO is
16

---

Published as a conference paper at ICLR 2026
Table The Acceleration Ratios of Model Scheduling and Token Pruning. Model scheduling yields the most prominent speedup, with redundancy increasing as task sequences grow longer. Optimizing single dimension reveals redundaney specific to it, suggesting that temporal and spatial accelerations are interdependent. SP-VLA exploits this interplay to achieve superior acceleration.
Pruning Rate (%, Intuitive Action Rate (%, ()
Method
Goal
Object
Spatial
Long
Average
Average Acc. (%, 1)
Ours
19.45 15.00
6.00 18.07
10.50/ 13.90
5.80/ / 19.64
10.44 16.65
74.90
w/o Pruning
0.00/ / 18.58
0.00 21.11
0.00/ / 15.41
0.00/ 28.00
0.00 20.78
73.98
Scheduling
19.66 0.00
/..00
22.70/ 00000
0.000
16.85 0.00
71.52
w/o Canny
16.42/ 11.42
10.10/ 18.13
17.40 11.13
17.30 13.20
15.31 13.47
23.93
Table 10: The results under different settings. SP-VLA maintains high accuracy across various acceleration ratios, demonstrating the strong robustness of our approach. For example, method not only improves accuracy under moderate acceleration (1.35 x), but also maintains competitive performance under higher speedup (1.5x) with only 3% accuracy drop.
Success Rate (%, 1) Speed up (1)
Method
Average
FLOPs (%,
Goal
Object
Spatial
Long
VLA
75.40
86,20
83.80
53.30
74.68
100.00
Ours-1
73.60/ / 1.66
82.40 / 1.44
80.00
1.47
51.60 / 1.42
71.90 1.50
66.51
Ours-2
72,20 / 1.58
83,60
1.36
81.40/
1.35
50,40 / 1.44
71.90
1.43
69.83
Ours-3
74.80 / 1.36
84.80
1.30
82.20
1.29
53.40/155 1.29
73.80
1.31
76.25
Ours-4
75.40 / 1.46
85.60
1.30
84.40 1.30
54.20 / 1.32
74.90 / 1.35
73.63
mainly used to evaluate the stability of SP-VLA across diverse tasks, whereas SimplerEnv focuses on generalization. As shown in Tables 1 and 3, SP- SP-VLA delivers strong results in both settings: 1.5x lossless acceleration on LIBERO and up to 2.4x acceleration with 6% performance gain SimplerEnv, underscoring its robustness.
A.5 THE ACCELERATION RATIOS OF MODEL SCHEDULING AND TOKEN PRUNING.
Using the same parameter settings as in Table 2, Table 9 presents the acceleration contributions of model scheduling and token pruning. Model scheduling achieves the highest accuracy at comparable acceleration levels, suggesting that much of the computational redundancy in VLA models arises from intuitive and deliberate actions. We further find that the proportion of inruitive actions grows with task length, from 18% in LIBERO-Spatin (1.18x speedup) to 28% in LIBERO-Long (1.39x acceleration). While individual techniques expose redundancy mainly along a single dimension-e.g. token pruning identifies 22.7% in LIBERO Spatial and model scheduling 21% in ABBERO-Object-- optimizes temporal and spatial redundancies. These dimensions are interdependent rather than orthogonal, and SP-VI.A effectively exploits this synergy achieve superior acceleration.
jointly
A.6 LIMITATION
In this work, we discovered and verified that the VLA model can be categorized into deliberative actions and intuitive actions. By leveraging this distinction, we propose a method that jointly schedules the model and prunes tokens for VLA models, achieving acceleration in both the temporal and spatial dimensions. However, our current exploration of intuitive action generation is limited to model lightweighting and remains preliminary. As a result, a complete separation between the generation of deliberative and intuitive actions has not yet been achieved. We believe that explicitly distinguishing these two types actions in the behavioral logic of VLA models will make them more human-lite llke and is crucial step toward achieving higher accuracy, faster inference, and lower energy consumption, with significant potential for future development. Therefore, this will be one of the key directions of future exploration.
17

---

Published as a conference paper at ICLR 2026
Table 11: The Acceleration Ratios of Model Scheduling and Token Pruning. Overall, model scheduling achieves higher acceleration ratios than token pruning, indicating that VLA contains significant temporal redundancy. By categorizing actions into deliberative and intuitive types, this redundancy can be efficiently addressed. Moreover, the varying tolerance of different tasks to the two acceleration methods suggests that these approaches are not orthogonal, but rather complementary, working together to achieve optimal performance.
Pruning Rate (%, t)/ Intuitive Action Rate (%, 1)
Method
Goal
Object
Spatial
Long
Average
Average Acc. (f)
Ours-1 1
19.45 / 15.00
6.00 / 18.07
10,50 / 13.90
5,80/ / 19.64
10.44 / 16.65
71.90
Ours-2
24.91 15.88
6.25 / 21.58
15.51 / 12.63
9.09 23.81
13.93 / 18.48
72.20
Ours-3
15.01 12.87
5.89 18.61
9.57 / 14.33
5.71 17.95
9.05 15.94
73.80
Ours-4
26.70/ 17.40
6.47 25.70
18.02 17.29
5.67 /
14.22 21.48
74.90
A.7 MORE EXPERIMENTAL RESULTS.
Tables 10 and 11 present the results of SP-VLA under different settings. Overall, SP-VLA maintains high accuracy across diverse acceleration ratios, demonstrating robust adaptability varying acceler- ation demands. For instance, at the 1.35 x acceleration rate, SP-VLA achieves an overall accuracy of 74.90% outperforming Open VL.A. This indicates that moderately reducing redundancy can help correct errors and improve performance. Even under 1.5x acceleration, the accuracy only drop 3%, highlighting the significant temporal and spatial redundancy present VLA models. These results highlight that SP-VLA achieves outstanding acceleration on VLA models, while also demonstrating strong robustness by maintaining stable performance across wide range of acceleration ratios.
In terms of acceleration contributions, model scheduling delivers markedly larger speedups than token pruning, consistently outperforming across all settings. This finding suggests that VLA execution involves high proportion of intuitive actions, revealing substantial computational redundancy. On the other hand, different tasks exhibit varying levels tolerance to accelcration methods. For example, the LIBERO- Goal task allows relatively large degree of token pruning, achieving 26.70% reduction while maintaining accuracy. In contrast, the LIBERO-Long task only supports 5.67% pruning rate, with most of the acceleration coming from intuitive action generation. This indicates that as task complexity increases, higher spatial perception capability is required from the VLA model. Furthermore, the acceleration methods are not entirely independent; instead, they work synergistically to achieve optimal acceleration performance.
To further demonstrate the generality and effectiveness of approach, we provide visualizations and task-wise speedup curves for various LIBERO tasks in Sections A.8 and A.9, respectively. These examples further illustrate that SP-VLA is well-suited for wide range of manipulation tasks and achieves remarkable acceleration performance.
A.8 MORE VISUALIZATION RESULTS.
18

---

Published as a conference paper at ICLR 2026
LIBERO-Spatial. Pick the black bowl between the plate and the ramekin and place on the plate.
SABERO-Spatial Pick the black bowl next to the ramekin and place it on the plate.
LIBERO-Spatial Pick up the black bowl from table center and place it the plate.
LIBERO- Spatial Pick up the black bowl the cookie box and place it the plate.
LIBERO-Spatinn Pick up the black bowl in the top drawer of the wooden cabinet and place it on the plate.
LIBERO-Spatial Pick up the black bowl on the ramekin and place on the plate.
LIBERO-Spatial Pick up the black bowl next to the cookic box and place the plate.
LIBERO-Spatin Pick up the black bowl the stove and place it on the plate.
LIBERO Spatial: Pick the black bowl next to the plate and place it on the plate.
LIBERO- SAERR--Spttall Pick the black bowl on the wooden cabinet and place it on the plate.
Figure 6: Visualization examples generated by SP-VLA on LIBERO-Spatial.
19

---

Published as a conference paper at ICLR 2026
LIBERO-Gone Open the middle drawer of the cabinet.
LIBERO-Goal: Put the bowl the stove.
I
LIBERO Goal: Put wine bottle on top of the cabinet.
LIBERO-Goal: Open the top drawer and put the bowl inside.
LIBERO-Goal: Put the bowl top of the cabinet.
LIBERO Goal: Push the plate to the front of the stove.
LIBERO-Goal, Put the cream cheese in the bowl.
LIBERO-Goal: Turn on the stove.
LIBERO-Goal: Put the bowl the plate.
LIBERO-Good Goal: Put the wine bottle the rack.
Figure 7: Visualization examples generated by SP-VLA LIBERO-Goal.
20

---

Published as a conference paper at ICLR 2026
Object: Pick up the alphabet soup and place it in the basket.
LIBERO Object: Pick up the cream cheese and place the basket.
SIBERO-Object Pick the salad dressing and place it in the basket.
LIBERO-Object: Pick up the bbq sauce and place it in the basket.
LIBERO-Object: Pick up the ketchup place it the basket.
LIBERO-Objer Pick up the tomato sauce and place it in the basket.
LIBERO-Object Objjct:: Pick up the butter and place it the basket.
Objeet- Pick up the milk and place it in the basket.
LIBERO-Object: Pick up the chocolate pudding and place it the basket.
LIBERO-Ob Object: Pick up the orange juice and place it in the basket.
Figure 8: Visualization examples generated by SP-VLA on LIBERO-Object.
21

---

Published as a conference paper at ICLR 2026
LIBERO-Long' Put both the alphabet soup and the tomato sauce the basket.
LIBERO-Long Put both the cream cheese box and the butter in the basket.
LIBERO-Long: Turn on the stove and the moka pot on it.
LIBERO-Long Put the black bowl in the bottom drawer of the cabinet and close it.
LIBERO-Long: Put the white mug on the left plate and put the yellow and white mug on the right plate.
LIBERO-Long: Pick up the book and place it in the back compartment of the caddy.
LIBERO-Long: Put the white mug the plate and put the chocolate pudding the right of the plate.
LIBERO-Long: Put both the alphabet soup and the cream cheese box in the basket.
LIBERO-Long: Put both moka pots the stove.
LIBERO-Long: Put the yellow and white mug in the microwave and close it.
Figure 9: Visualization examples generated by SP-VLA on LIBERO-Long.
22

---

Published as a conference paper at ICLR 2026
A.9 ACTION DYNAMICS ACROSS DIFFERENT TASKS.
Targeting
Grasping
Moving
Putting
Targeting
Grasping
Moving
Putting
1.0
Velocity
Velocity
0.5
0.5
0.
Velocity
Velocity
04
02
0.
1.
Velocity.
Velocity
Speed Change
Speed
Timestep
Timestep
(a) LIBERO-Spatial Pick up the black bowl between the plate and the ramekin and place it the plate.
(b) LIBERO-Spatial Pick up the black bowl next to the ramekin and place it on the plate.
Targeting Grasping
Moving
Putting
Targeting
Grasping
Moving
Putting
Velocity
Velecity
10:
0.0
Velocity
Velecity
03:
0.23
Velocity
3.8
Velecity
10
8.5
Speed Change
Speed Change
Timestep
Timestep
(c) LIBERO- Spatial:: Pick up the black bowl from table center and place it on the plate.
(d) LIBERO-Spatial Pick up the black bowl on the cookie box and place it on the plate.
Targeting
Grasping
Moving
Putting
Targeting Grasping
Putting
3.4
Velocity
Velocity
355
Velocity
y
Velocity
0.75
0.5
0.2
0.0
0.0
Velocity
2
Velocity
0.5
Speed Change
Speed Change
100
140
100
120
Timestep
Timestep
(e) LIBERO-Spatial: Pick up the black bowl in the top drawer of the wonden cabinet and place the plate.
(f) LIBERO-Spatial Pick the black bowl on the ramekin and place it on the plate.
Figure 10: Visualizations of SP-VLA on the first 6 LIBERO-Spatial tasks.
23

---

Published as a conference paper at ICLR 2026
Targeting
Grasping
Moving
Putting
Targeting Grasping
Moving
Putting
0.75
Velocity
Velocity.
0.50
0.5
0.73
Velocity
Velacity
0.5
023
0.00
Veloctty
Velocity
Speed Change
Speed Change
100
120
40
60
100
1200
Timestep
Timestep
(a) LIBERO-Spatial up the black bowl next to the cookie box and place it on the plate.
(b) LIBERO-Spatial Pick up the black bowl on the stove and place it on the plate.
Targeting
Grasping
Moving
Putting
Targeting
Grasping
Moving
Putting
Velocity
Velocity
0.5
1.0
Velocity
Velocity
0.3
1.0
Velocity
Velocity
0.5
Speed Change
Speed Change
0.0
Timestep
Timestep
(c) LIBERO-Spatin Pick up the black bowl next the plate and place plate.
LIBERO-Spatia Pick up the black bowl the
wooden cabinet and place on plate.
Figure 11: Visualizations of SP-VLA on the remaining 4 LIBERO-Spatia tasks.
24

---

Published as a conference paper at ICLR 2026
Targeting
Grasping
Moving
Targeting
Grasping
Moving
Putting
1.0
Velacity
0.50
x
Velocity
8.
0.0
1.-
Velocity
y
Velocity
0.3
0.5
10
1.0
Velocity
2
Velocity
0.0
Speed Change
Timestep
Timestep
(a) LIBERO-Goal: Open the middle drawer of the
(b) LIBERO-Goal Put the bowl on the stove.
cabinet.
Targeting Grasping
Moving
Putting
Targeting Grasping
Moving
Putting
Velocity
Velocity
0.50
0.2
0.00
Velocity
y
Velocity
W/
05
10
Velocity
Velocity
00
Speed Change
Speed Change
Timestep
Timestep
(e) LIBERO-Goal: Put the wine bottle on top of the
LIBERO-Goal Open the top drawer and put the
cahinet.
howl inside.
Targeting Grasping
Moving
Putting
Targeting Grasping
Moving
Putting
Velocity
X
Velocity.
0.5
1.0
Velocity
Velocity
0.5
0.5
Velocity
2
Velocity
0.
Speed Change
Speed Change
0.
100
120
160
50
100
150
250
Timestap
Timestap
(e) LIBERO-Goal: Put the bowl on top of the
(1) LIBERO-Goal Push the plate to the front of the stove.
cabinet.
Figure 12: Visualizations of SP-VLA on the first L. LIBERO-Goal tasks.
25

---

Published as a conference paper at ICLR 2026
Targeting Grasping
Moving
Putting
Targeling Grasping
Moving
Putting
0.75
Velocity
Velocity
0.90
0.23
0.75
Velocity
Velocity
1.0
14
Velocity:
Velocity
Change
Speed
56
Timestep
Timestep
(a) LIBERO-Goal Put the cream cheese the bowl.
(b) LIBERO-Goal: Turn the
Targeting Grasping
Moving
Putting
Targeting
Grasping
Moving
Putting
1.0
Velocity
Velocity
05
06
Velociy Pogto
Velocity
y
0.30
0.00
Velocity
Velocity
Speed Change
Speed Change
100
120
140
100
150
200
Timestep
Timestep
(c) LIBERO-Goal: Put the bowl on the plate.
(d) LIBERO-Goal: Put the wine bottle on the rack.
Figure 13: Visualizations of SP-VLA the remaining 4 LIBERO-GoR tasks.
26

---

Published as a conference paper at ICLR 2026
Targeling Gresping
Moving
Putling
Targeting Grasping
Moving Putting
Velocity
Velocity
0.50
0.29
0.00
Velocity
y
Velocity
0.5
4.0
Velocity
2
Velocity
0.
Speed Change
Speed Change
Timestep
Timestep
(a) LIBERO-Object Pick up the alphabet soup and place it in the basket.
(b) LIBERO-Object Pick up the cream cheese and place it in the basket.
Targeting Grasping
Putting
Targeting Graspling
Moving
Putting
Velocity
Velocity
03
Velocity
Velocity
0.5
0.5
0.0
10
Velacity
0.75
Velocity
0.50
0.25
Speed Change
0.00
Speed
80
100
320
140
100
50
200
150
375
Timestep
Timestep
(c) LIBERO-Object Pick up the salad dressing and place it the basket.
LIBERO-Objer Pick up the bbq sauce and
place the basket.
Targeting Grasping
Moving
Putting
Targeting Grasping
Moving
Putting
0.4
3.5
Velocity
Velocity
20
Velocity
Velocity
y
Velocity
Velocity
0.50
023
0.0
Speed Change
0.00
Speed Changer
100
125
190
Timestep
Timestep
(c) LIBERO-Object Pick up the ketchup and place it in the basket.
(f) LIBERO-Object: Pick up the tomato sauce and place it in the basket.
Figure 14: Visualizations of SP-VLA on the first LIBERO-Objec tasks.
27

---

Published as a conference paper at ICLR 2026
Targeting Grasping
Moving
Putting
Targeling Grasping
Moving
Putting
Velocity
Velecity,
02
0.0
Velocity
Velocity
05
00
Velocity.
2
Velocity
0.
0.
Speed Change
Spoed Change
Timestep
Timestep
(a) LIBERO-Object Pick up the butter and place it
(b) LIBERO-Object Pick up the milk and place it in
the basket.
the basket.
Targeting Grasping
Moving
Putting
Targeting
Grasping
Moving
Putting
Velocity
Velogity
60
0.
Velocity
y
Velogity
0.5
0.3
1.0
Velocity
2
Velogity
0.5
Wpeed Change
Speed Change
0.6
100
125
150
125
100
120
140
Timestep
Timestep
(c) LIBERO-Object: Pick up the chocolate pudding and place it in the basket.
(d) CIEEOO-Object: Pick up the orange juice and
place in the basket.
Figure 15: Visualizations of SP-VLA on the remaining LIBERO-Object tasks.
28

---

Published as a conference paper at ICLR 2026
Grasping
Putting
Grasping Putting Targeting
Targeting
Moving
Moving
0.75
Velocity
Velocity
0.5
0.00
10
Velocity
Velocity
0.0
Velocity
Velocity
0.0
200
400
Speed Change
Timestep
Speed Change
Timestep
(a) LIBERO-Long: Put both the alphabet soup and the tomato sauce in the basket.
(b) LIBERO-Long: Put both the cream cheese box and the butter in the basket.
Grasping
Grasping
Putting
Targeting
Moving Putting
Targeting
Moving
0.75
Velocity
Velocity
0.00
Velocity
0.50
Velocity
0.00
Veloclby
0.50
0.75
Velacity
0.00
0.00
Speed Change
100
190
200
250
300
350
600
100
150
200
200
350
Speed Change
Timestep
Timestep
(c) LIBERO-Long: Turn on the stove put moka pot on it.
(d) LIBERO-Long: Put black bowl in the bottom drawer of the cabinet and close it.
Grasping Putting
Grasping
Targeting Moving
Targeting
Moving
Putting
Velocity
Velocity
82
8.0
Velocity
0.0
Velocity
0.50
0.5
0.0
Velacity
0.50
Velocity
0.5
Speed Change
Timestep
Speed Change
Timestep
(e) LIBERO-Long: Put the white mug on the left plate and put the yellow and white mug the right plate.
(i) LIBERO-Long: Pick up the book and place it in the back compartment of the caddy.
Figure 16: Visualizations of SP-VLA on the first 6 LIBERO-Long tasks.
29

---

Published as a conference paper at ICLR 2026
Graaping Putting
Grasping Putting Targeting Moving 0.25
Moving Targeting
Velocity
Velocity
0.25
0.0
0.00
Velocity
Velocity
80
Velocity
00
Speed
150
Timestep
250
Speed Chinge
150
Timestep
400
(a) LIBERO-Long: Put the white mug on the plate and put the chocolate pudding the right of the plate.
(b) LIBERO-Long Put both the alphahet soup and the cream cheese hox in the basket.
Grasping
Grasping
Putting
Targeting
Moving Putting
Targeting
Moving
0.6
Velocity
Velocity
Velocity
0.5
y
Velocity
0.90
0.23
Velocity
Velecis
0.50
00
Speed Change
0.00
30
200
150
220
150
300
250
400
100
150
200
250
200
350
Timestep
Speed Change
Timestep
(c) LIBERO-Long. both moka pots on stove.
(d) L.BERR-LLong: Put yellow white mug
the microwave and close it.
Figure 17: Visualizations of SP-VLA on the remaining LIBERO-Long tasks.
30


Reviews:
[{"review_id": "iPL7zog3cD", "reviewer": "Reviewer_4TvA", "summary": "The paper presents a model scheduling and token pruning approach to make VLA. The main idea is categorizing VLA actions as deliberative and intuitive and then using a smaller model for the intuitive actions. Additionally the paper also incorporates a token pruning approach where tokens are pruned based on semantic and spatial relevance to the task. The paper shows that jointly applying both model scheduling and token pruning helps achieve significant improvements in VLA inference.", "questions": "Please see the weaknesses section for my questions. The paper presents a nice idea for speeding up VLA inference however, I have concerns regarding the hyper-parameters selection and its transfer to real-world setting therefore I’m leaning towards a reject for now. Will be happy to change my mind if the authors can answer questions and other reviewers do not bring major concerns that I might have missed.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper presents a simple method to speed VLA inference based on a simple observation that most actions predicted by the VLA do not require the full processing power of a VLA (intuitive actions) and can rather be estimated using a super simple ridge regression. \n2. The method shows significant improvement in run-time of VLA on 2 simulated benchmarks.", "weaknesses": "***1. How are hyper-parameters chosen?***\n\nMy biggest concern with this paper is that performance of both model scheduling and token pruning are heavily reliant on the hyper-parameters and there is not much detail on how they are chosen. \n\nAre these simulator/task-dependent? In which case, do you select them on a subset of data and then evaluate on a larger set to see if they transfer?\n\nHow can these parameters be selected for real-world tasks where evaluation is not cheap to run and each task might require a different set of hyper-parameters?\n\n\n\n***2. Why does performance improve with SP-VLA?***\n\nThe paper does not explain much how/why (they mention error correction which is not explained well) performance would improve when using model scheduling and token-pruning where intuitively one would think that performance should drop (at least slightly as we are pruning some information).\n\n\n***3. No real-world results?***\n\nIt would be interesting to see how the method holds in real-world evaluations. It would also be interesting to see if there’s an efficient way to estimate the hyper-parameters required for real-world tasks or if the hyper-parameters discovered in simulation transfer to the real-world setting as well.", "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": "JSjDUTXAsr", "reviewer": "Reviewer_7oJM", "summary": "The paper introduces SP-VLA, a unified framework for accelerating Vision-Language-Action (VLA) models. The authors identify two main sources of redundancy in VLA models: temporal redundancy in action generation and spatial redundancy in visual input. To reduce temporal redundancy, the authors distinguish between deliberative actions, which are handled by the full VLA model, and intuitive actions, approximated by a lightweight generator based on ridge regression. To reduce spatial redundancy, the method prunes visual tokens before feeding them into the language model, by combining semantic attention scores and Canny edge detection. The approach is validated on LIBERO and SimplerEnv, showing latency and control frequency improvements, while maintaining high performance.", "questions": "* How would the method work in conjunction with different action prediction heads? E.g. diffusion / flow matching policies\n* How can the method deal with multiple cameras?\n* How can we train the model to handle multiple embodiments (i.e. robotic setups) at once?\n\nThere are several typos in the paper:\n* “navigatio and medical robotics” → should be “navigation and medical robotics”.\n* “Accleration for Vision-Language-Action Models.” → should be “Acceleration for Vision-Language-Action Models.”\n* “LANTENCY AND FREQUENCY” (Appendix) → should be “LATENCY AND FREQUENCY”.\nand many more (including \"Ride Regression\" in Figure 3)\n\nI would recommend the authors to use a grammar checker and thoroughly check their manuscript.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "* **Perception tokens pruning**: the spatio-semantic dual-aware token pruning strategy is well-motivated and empirically validated. By combining semantic attention with spatial cues, the method preserves critical spatial information achieves faster inference without significant accuracy loss.\n* **Comprehensive experiments**: The paper provides extensive experiments on standard benchmarks (LIBERO, SimplerEnv), including ablation studies and comparisons to relevant baselines.", "weaknesses": "* **Loosely-connected contributions**: The paper presents two main ideas (action scheduling and token pruning) that are only loosely connected, apart from the common goal.\n* **Limited generality**: the introduced novelties are \"simpler\" than the VLA model and they introduce a new set of hyperparameters, which hinders generality of the approach. This limitation particularly affects the action part, where different embodiments may have very different action spaces and thus, defining hyperparameters might be more difficult", "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": "GLeHrzTKgX", "reviewer": "Reviewer_XzJ6", "summary": "SP-VLA accelerates Vision-Language-Action by (i) temporal scheduling between a full VLA policy and a lightweight ridge-based action extrapolator triggered by speed/gating signals, and (ii) order-preserving token pruning that keeps both semantic (accumulated attention) and spatial (Canny edges) tokens with a speed-adaptive keep ratio. Across simulated suites and backbones, it reports considerable speedup without accuracy loss.", "questions": "Overall, this paper hits a real pain point in VLA—wasted compute across time and space—and the solution is practical enough to drop into existing stacks. I have a few comments and questions as follow.\n\nHow do you handle switch latency and thrashing between models? What’s the impact on P95/P99 latency?\n\nHow much of the speedup remains if you add tail-latency limits or minimum dwell times between switches?\n\nHow often do you fall back to the full VLA under strong nonlinearity, and how quickly does the system recover from a bad extrapolation?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Interesting idea that targets time + space waste; easy to plug into different VLA stacks.\n\n2. Solid gains with small or no accuracy drop; ablations back up the design.\n\n3. Explains why semantics-only pruning breaks VLA (loses spatial order).", "weaknesses": "1. Relies on a few heuristic knobs (speed window, buffer length, gating τ); some sensitivity.\n\n2. The lightweight head assumes near-linear short-horizon motion; can fail under contact/perturbations.\n\n3. Canny edge detection can be fragile in the presence of lighting and material noise. Additionally, there is limited evidence from real-robot experiments and little information on tail latency and energy consumption.", "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": "6G8qpQbmRV", "reviewer": "Reviewer_2cVD", "summary": "The paper proposes SP-VLA, a unified acceleration framework for Vision-Language-Action (VLA) models.\nIt combines:(1) Model Scheduling – dynamically switching between a full-scale VLA model and a lightweight ridge-regression-based generator depending on whether the action is “deliberative” or “intuitive,” to reduce temporal redundancy; and(2) Spatio-Semantic Token Pruning – pruning vision tokens using accumulated attention scores and Canny-edge information to remove spatial redundancy. Experiments on LIBERO and SimplerEnv demonstrate 1.5–2.4× inference acceleration with nearly lossless task performance, and improved inference frequency and latency.", "questions": "1. Could you explain how the main hyperparameters (e.g., speed thresholds, pruning ratios, buffer size) were determined in practice?\nWere they tuned by trial-and-error, grid search, or guided by theoretical intuition or prior experiments? Are these parameter settings consistent across tasks and environments, or do they need adjustment for each scenario? \n\n2. Have you attempted to deploy SP-VLA on a physical robotic platform, or at least measured whether the acceleration results reported in simulation differ from those on real hardware?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Novel and conceptually clear idea**\n\nThe paper is among the first to jointly address temporal and spatial redundancies in VLA models.\nThe analogy to human dual-process control (deliberative vs. intuitive actions) is original and intuitively compelling, offering a behavioral perspective on model efficiency.\n\n2. **Comprehensive empirical validation**\n\nResults are reported across multiple VLA backbones (OpenVLA, CogACT) and environments (LIBERO, SimplerEnv) with consistent performance gains.\nAblation studies, sensitivity analyses, and visualizations are well-structured and strengthen empirical credibility.", "weaknesses": "1. **Over-reliance on handcrafted heuristics**\n\nBoth the scheduling and pruning modules rely on manually designed heuristics—velocity thresholds, ridge regression fitting, Canny edges, and fixed attention thresholds—rather than adaptive or learnable components. This constrains scalability and contrasts with current trends toward learned compression/scheduling in multimodal LLM research.\n\n2. **Generalization and robustness not sufficiently validated**\n\nThe method’s stability under varying architectures, task types, or sensor conditions is not explored. Key hyperparameters (speed thresholds, pruning ratios, deliberative/intuitive ratio) might require re-tuning, reducing transferability. A cross-task or cross-model evaluation would greatly enhance the paper’s impact.", "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)