QVLA: Not All Channels Are Equal in Vision-Language-Action Model's Quantization
Authors: Yuhao Xu
Yantai Yang
Zhenyang Fan
Yufan Liu
Yuming Li
Bing Li
Zhipeng Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Vision-Language-Action Models
Embodied AI
Model Quantization
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We present the first systematic analysis of quantization challenges in Vision-Language-Action (VLA) models, designing and implementing a comprehensive quantization framework specifically for this architecture.

Abstract: The advent of Vision-Language-Action (VLA) models represents a significant leap for embodied intelligence, yet their immense computational demands critically hinder deployment on resource-constrained robotic platforms. Intuitively, low-bit quantization is a prevalent and preferred technique for large-scale model compression. However, we find that a systematic analysis of VLA model's quantization is fundamentally lacking. We argue that naively applying uniform-bit quantization from Large Language Models (LLMs) to robotics is flawed, as these methods prioritize passive data fidelity while ignoring how minor action deviations compound into catastrophic task failures. To bridge this gap, we introduce AutoQVLA, the first action-centric quantization framework specifically designed for embodied control. In a sharp departure from the rigid, uniform-bit quantization of LLM-based methods, AutoQVLA introduces a highly granular, channel-wise bit allocation strategy. Its core mechanism is to directly measure the final action-space sensitivity when quantizing each individual channel to various bit-widths. This process yields a precise, per-channel importance metric that guides a global optimization, which elegantly unifies quantization and pruning (0-bit) into a single, cohesive framework. Extensive evaluations on different baselines demonstrate the superiority of our approach. In the LIBERO, the quantization version of OpenVLA-OFT with our method requires only 29.2% of the original model's VRAM while maintaining 98.9% of its original performance and achieving a 1.49$\times$ speedup. This translates to a 22.6% performance improvement over the LLM-derived method SmoothQuant. Our work establishes a new, principled foundation for compressing VLA models in robotics, paving the way for deploying powerful, large-scale models on real-world hardware.

Published as a conference paper at ICLR 2026
QVLA: NOT ALL CHANNELS ARE EQUAL IN VISION- LANGUAGE-ACTION MODEL'S QUANTIZATION
Yuhao X3 Yantai Yang!. Zhenyang Fan Yuming Li Bing Li Zhipeng Zhang" If
Yufan Liu3.41
1 LautoLab, School of Artificial Intelligence, Shanghai Jiao Tong University 2 Anyverse Dynamics
Sttate Key Laboratory of Multimodal Artificial Intelligence Systems,
Institute of Automation, Chinese Academy of Sciences
"School of Artificial Intelligence, University of Chinese Academy of Sciences
Terminal Technology Department, Alipay, Ant Group
Code: https//giiibb.com/Auuuaaabbbbbbb SALSSUUUVVAAASU
ABSTRACT
The advent of LssionLaanuuage-AAtion (VLA) models represents significant leap for embodied intelligence, yet their immense computational demands crit- ically hinder deployment on resource-constrained robotic platforms. Intuitively, low-bit quantization is a prevalent and preferred technique for large-scale model compression. However, we find that systematic amalysis of VLA model's quanti- zation fundamentally lacking. We argue that naively applying uniform-bi quan- tization from Large Language Models (LLMs) to robotics is flawed, as these meth- ods prioritize passive data fidelity while ignoring how minor action deviations compound into catastrophic task failures. To bridge this gap, we introduce QVLA, the first action-centric quantization framework specifically designed for embodied control. In sharp departure from the rigid, uniform-bit quantization of LLM- based methods, QVLA introduces a highly granular, chanmel-wist bit allocation strategy. Its core mechanism to directly measure the final action-space sensitiv- ity when quantizing each individual channel to various bit-widths. This process yields a precise, per-channel importance metric that guides global optimization, which elegantly unifies quantization and pruning (0-bit) into single, cohesive framework. Extensive evaluations on different baselines demonstrate the superi- ority of our approach. In the LIBERO. the quantization version of Open with our method requires only 29.2% of the original model's VRAM while main- taining 98.9% % of its original performance and achieving 1.49 speedup. This translates to a 22.6% % performance improvement over the LLMM-derived method SmoothQuant. Our work establishes new, principled foundation for compress- ing VLA models in robotics, paving the way for deploying powerful, large-scale models on real-world hardware.
INTRODUCTION
The rapid evolution of foundation models (Touvron et al., 2023a b) has significantly advanced embodied intelligence, empowering LasionLLanguage-Action (VLA) models like OpenVLA (Kim) ct al. 2024) to synthesize complex, executable actions from visual inputs and linguistic directives. These models enhance cross-task generalization and semantic reasoning, elevating the efficacy of robotic manipulation. However, their immense computational and memory demands, which often exceed 14 GB in standard half- prefispreiiion for 7B model, present critical barrier to deployment. For instance, running such model on a widely-used robotic platform like the NVIDIA Jetson AGX Orin can result in inference latencies of several hundred milliseconds per action, far too slow for the fluid, real-time control required in dynamic environments. This performance gap necessitates aggressive compression methodologics, such as pruning (Liu et al. 2022b) Ruan et al. 2020 202T), knowledge
distillation (Liu al. 2019 2022a) and quantization (Xiao et al. 2022 Lin al. 2023) to achieve
"Equal contribution. 'Corresponding author.
Work performed during remote internship SITU.

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Published as a conference paper at ICLR 2026
practical inference speeds while sustaining precise control Yang et al. 2025 Wen et al. 2024 Song et al. 2025b a). Surprisingly, while low-bit quantization is a well-established technique extensively studied in Large Language Models (LLMs), we find there has been systematic analysis of its unique impacts and trade-offs when applied specifically to VLA methods.
This gap is not merely academic oversight but critical barrier, as naively applying existing quantization techniques ignores the fundamental distinctions between VLA models and their LLMs or Multimodal Large Language Models (MLLMs) counterparts. Indeed, aforementioned, the development of quantization techniques has been predominantly driven by the requirements of LLMs (Frantar et al., [022] Li et al., 2021] Nagel et al.) 2020) and general purposs MLLMs (Wang et al., 2024). These approaches are optimized to preserve text perplexity or visual feature fidelity, often using proxy loss functions. In stark contrast, the output of a VIAA model is not passive text or a label, but a sequence of continuous action values that directly interface with the physical world. In this closed-loop setting, even subtle quantization-induee errors in action outputs, that may be imperceptible in standard benchmarks, can be amplified by physical dynamics and contact forces. Over a long-horizon task, these errors accumulate autoregressively, leading to catastrophic failures such unstable grasps or significant trajectory deviations (as illustrated in Fig. 33) Consequently, directly porting quantization frameworks designed for passive data processing is fundamentally ill- suited for the demands of active, embodied control, it often undermines the requisite stability and precision. This naturally raises the question that How should one design a quantization method specifically tailored to the unique demands of VLA models?
Before answering this question, we first revisit the predominant paradigms in model quantization. Recent scholarly efforts, exemplified by representative works such as SmoothQuant (Xiao et al.) 2022). have primarily focused on outlier management. These methods employ techniques like ro- tations, permutations, or saliency-based protections to mitigate the dominance of extreme values in quantization step sizes, thereby enhancing low-bit performance. However, this outlier-centric paradigm proves insufficient for VIAA models, where mross-modal alignment and action decoding interfaces (e.g., projectors and action heads) exhibit acute sensitivity to perturbations, while long- horizon tasks amplify even minor initial errors (see analysis in Sec. (3.2 rently, in industrial practice, module-level mixed precision has emerged quantizing vision encoders to 4-bit while preserving language backbones at 8-bit. Yet, this coarse- grained approach lacks the necessary precision. Our analysis, as detailed in Sec. 3.2] reveals signif- icant intra-layer channel heterogeneity. Specifically, individual channels contribute variably to the final VLA outputs, which is a critical distinction that conventional methodologies fail address.
S.2.3.2) and again Fig. 3 Concur- compromise, such as
Building upon this foundational analysis, we present QVLA, an advanced, action-centric frame- work for channel-wise quantization. To our knowledge, this marks the first systematic exploration of quantization specifically tailored for VLA architectures. Our method directly anchors the quan- tization objective within the action space, rather than the internal representation. Moreover, our approach naturally unifies quantization and pruming (0-bit) into single process, enabling fine- grained, per-channel bit allocation. These advancements are achieved in two key steps: (1) Action- space sensitivity estimation that measures how much quantizing each channel affects the final action output with the proposed sensitivity metrics. For efficiency, we use a fast first-order proxy based on Taylor-series approximation to identify the most sensitive channels. (2) Optimal bit allo- cation that assigns the final bit-widths in {0, 2, 4, 8, 16) to each channel with the proposed global greedy demotion algorithm, starting from full precision and progressively lowering the bit- width of the least sensitive ones until the budget is met. Experiments on Open (Kim et al. 2024) and Open VLA-OFT (Kim et al.) baselines validate the efficacy of QVLA. In the LIBERO environ- requires only 29.2% % of the original model's VRAM while
ment, our method on the VpenVLA-OO maintaining 98.9% % of its original performance and achieving a 1.49 speedup.
Our contributions include: We conduct the first systematic analysis of quantization challenges unique to VLA models. Our findings reveal why existing paradigms fail and establish that aligning quantization with the action space is foundational principle for effective compression in embodied AI. We propose QVLA, novel thannel-wise framework that uniquely uses action-space sensitiv- ity to guide bit allocation, cohesively unifying weight quantization and pruning (0-bit). Through extensive evaluations on VLe and OpenVLA-OF OLA-OFT baselines, we demonstrate that QVLA sig- nificantly outperforms methods adapted from LLMs and MLLMs. At equivalent average bit- widths, our framework achieves substantially lower action errors and higher task success rates, validating its efficacy for robotics in cosurce-constrained environments.
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2
RELATED WORK
Lisuun-Languagg--cctinn models. Vision-LanguaageAAAtionn (VLA) models represent a dominant paradigm for generalist robotic control, learning direct policies that map high-dimensional visual observations and language instructions to low-level motor commands. Methodologies for VLA de- velopment have largely bifurcated based on the action decoding strategy. The first, exemplified by models like RT-2 (Zitkovich et al. 2023), OpenVLA (Kim et al.) 2024]. and UniVLA (Bu et al.) 2025], discretizes the continuous action space, casting control as a sequence-to-see to-seuence problem. Conversely, a second line of work prioritizes temporal fidelity and high-frequency control by model- ing actions the continuous domain. This is typically achieved with powerful generative decoders, such as the diffusion policies in Octo (Octo Model Team et al. 2024) and RDT-IB (Liu et al., 2024b) or the fow-matching network in To (Black et 2024)) Despite their distinct advantages, both approaches are encumbered by a substantial computational footprint (Shukor et al. 2025 Yang et at. 22.25), rendering their large-scale architectures prohibitive for real-time execution on resource- constrained robotics hardware. While architectural compression, TinyVLA (Wen al. 2024), offers a partial solution, a more fundamental optimization strategy from the broader deep learning field, ie., low-bit quantization, remains conspicuously under-explored within embodied AI. This work aims to fill this critical gap, positing that quantization is not merely an incremental optimization but a foundational component required unlock the practical deployment of generalist VLA models.
Quantization methods. Model quantization is connerstone technique for efficient deep learning deployment, reducing memory footprint and computational latency by representing weights and acti- vations with low-bit integers. The central challenge lies in minimizing the ensuing accuracy degrada- tion. Two primary methodologies dominate the field, i.e., Post-Training Quantization (PTQ) (Frantar Li et al. [022 Nagel et al. 2020) and Quantization-Awat Training (QAT) (Jacob al. Esser et al. 2020). PTQ offers a low-cost solution by quantizing a pretrained model with a small calibration set and no retraining. In contrast, QAT simulates the effects of quantization during training, allowing the model to adapt its parameters to mitigate precision loss. Recent advances have focused on mitigating the challenges of quantizing large models, particularly the presence of out- liers and activation -induced quantization difficulties. Methods like SmoothQuant (Xiao et al.) 2022) re-scale weights and activations to create more favorable quantization landscape, while AWQ Q (Lin) et al. 2023) preserves salient weights and OmniQuant (Shao et al. (024) smooths weight and acti- vation outliers. To handle pernicious outliers in weights and activations, another research thrust em- ploys learned rotations permutations to redistribute value distributions, exemplified by methods like QuaRot (Ashkboos et al. 2024) and SpinQuant (Liu et al. (024c),, enabling robust 4-bit quanti- zation. However, common limitation across these methods is the assumption of uniform precision, where a single bit-width is applied globally or, at best, per layer (e.g., HAWQ (Dong et al.) 2019 2020)). This coarse- gratined approach is fundamentally misaligned with the requirements of embod- ied policies. VLA models exhibit heterogeneous sensitivity across their architecture. For instance, subtle shifts in oetion-generation layers can lead to catastrophic failures in control, fragility not typically observed in standard perception or language tasks. Motivated by this critical shortcoming, we propose a grained, action-guided, channel-wise quantization framework.
et al. 2022
2018
METHOD
3.1 PRELIMINARIES
Lision-Language-Ace (VLA) models. A VLA model governs the behavior of an embodied agent by defining policy, denoted as II, that maps dime-dimensional sensory inputs and lan- guage directive to sequence of actions. The model operates within sequential decision-making framework. Formally, at each discrete timestep i, the agent perceives its environment through visual observations V. (e.g., RGB images) and receives time-invarian language instruction (prompt) p. The policy's 's role is to predict an action sequence A, (e.g., end-effector poses or joint velocities),
I I (Acl p).
The parameter set 0 typically comprises: vision encoders Quis that processes the high- nigh-dimensional visual inputs V, into compact feature representations; a projection layer éproy that maps these visual features into the multimodal embedding space shared with the language modality; Large
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191
Viscn Enunder
Pheyand
Langagy Madele
Autos Baa
Action ctror
Actiom
12'4
Video
Prijeder
Fanguage middii Action head
3.74 1.00 1:24
Global channel index (aggregated)
(a) Quantization Results Across Modules
(b) Quantization Results Across Channels
Quantitative analysis of quantization sensitivity in VLA models. (a) Per-module analysis shows that the projector and action head are significantly more sensitive quantization. (b) Per- channel analysis demonstrates highly heterogeneous sensitivity within modules. These findings collectively motivate our adaptive precision, channel-level quantization approach.
Figure/ I:
Language Model (LLM) decoder 0im. which serves as the core reasoning engine, contextualizing the visual percepts with the task prompt p; and action decoder Pact that translates the final latent representation from the LLM into an executable action sequence At. The learnable parameters / = {Onis, Oproj Oumm Onet} } of this policy are the subject of our investigation into model compression via quantization. A central challenge, which the VLA quantization method is designed to address, the pronounced sensitivity of the policy's action output, Ar, perturbations in its parameters 0.
Model quantization. Quantization is the process of mapping the continuous, full-precision param- eter set 0 to discrete, low-precision set Ô by quantization function Ó Q(0). The fundamental objective of quantization is to find an optimal mapping Q" that minimizes the performance degra- dation caused by the reduced precision. In our VLA context, this is equivalent to minimizing the divergence between the action distributions of the original policy 1I and the quantized policy llg.
Q' arg min (V..D.H.)DD [De [ll(aa/Ve.2 H.) || IQq (Ar Hc))]
(2)
where 'D is the data distribution and D denotes the Kullback-Leible divergence. Conventional quantization approaches usually apply uniform bit-width across all parameters. In such scheme, for a given weight tensor W from a linear transformation Y XW + b, the quantization process involves computing a scaling factor aw for the entire tensor and then mapping the full-precision values single -iit integers, using nearest rounding and clamping function,
W, 4 = clamp
W ow
2k
22--1111 11)
The de-quantized weights W~ Wg aw are then used approximate the original computation,
Le., Y +W+b, where X is the similarly quantized activation tensor. While simple to implement, this uniform strategy does not account for the heterogeneous sensitivity of different parameters to quantization noise. Therefore, our work is to find more effective approximation of Q" beyond the uniform bit-width that preserves the crucial behavioral characteristics of the agent.
3.2 SENSITIVITY ANALYSIS
Not all modules are equal. As previously delineated, VLA models possess modular architecture A naive application of uniform bit-width quantization across these diverse modules yields subop- timal results. Our empirical investigation, summarized in Fig. [a), systematically illustrates this disparity by isolating the quantization of each module and measuring the resultant impact on task performance. More concretely, the vision encoder (0,,) demonstrates considerable robustness than other components. This resilience likely stems from the high-dimensional and often redundant na- ture of visual input, where feature representations are more tolerant to perturbations. In contrast, the language module (0:) proves to be more vulnerable, as has also been noted in prior work (Jiang et al. 2025 Park et al. 2024). Quantizing this component to the same bit-width leads to a more pronounced performance degradation. Most critically, the modsl interfaces, i.e., the projector (0,,) and the action head (f.). exhibit the most acute sensitivity. Aggressive quantization these modules precipitates severe, often catastrophic, decline in performance. This is because these
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components serve as the final nexus for translating multimodal understanding into physical action. Any perturbations introduced here propagate directly and without mitigation to the output action distribution, leading to significant and often erroneous deviations in the agent's behavior. These findings underscore that an adaptive quantization strategy is not merely beneficial but essential for preserving the functional integrity of VLA models at low bit-widths.
Not all channels are equal. Motivated by this module-level disparity, we extend our analysis to finer granularity, revealing a pronounced heterogeneity even among channels within the same layer. Here, a "channel" refers to an output channel in convolutional layer or a row in the weight matrix of a linear layer. As illustrated in Fig. [ the impact of quantization on action errors is not uniformly distributed across channels. This observation highlights the inherent limitations of uniform bit allocation schemes (whether global or per-layer) and strongly motivates a more nuanced, per-channel mixed-ppeeistee strategy, which could include pruning (i.e., O-bit quantization) for the least sensitive channels optimize the computational budget.
To effectively identify these salient channels, we propose novel sensitivity metric grounded directly in the action space rather than the intermediate feature space. More specifically, for a given layer I and a specific channel within it, we isolate its impact by quantizing only that channel to a bit- width E {0, 2, 4, 8, 16). while all other parameters remain in full precision. We then define the single-step action sensitivity as the expected squared L2 norm of the resulting action deviation,
(b) $1. = EEWD
|| (V.1) 1
where A/00 (z) is the perturbed action generated with the quantized channel, and (x is the ref- erence action from the full-precision model. However, single-step error metrics like $1,c may not fully capture the error accumulation that occurs in long-horizon, autoregressive tasks. A small, ini- tial error can compound over a sequence of actions. To account for this, we introduce a cumulative sensitivity metric, which measures the total deviation over entire episode:
(b)
(b)
E
Σ
(5)
(bb) This cumulative metrie, St. naturally exhibits a stronger correlation with ultimate task success.
3.3 THE PROPOSED QVLA
Building upon the insights from our multi- colti-granularitt sensitivity analysis, we introduce QVLA, quantization framework specifically designed to address the acute sensitivity of VLA outputs and ensure robust action generation. Departing from conventional LLMs and MLLMs quantiza- tion approaches that focus reconstructing internal feature representations, the proposed QVLA framework emphasizes the preservation of action fidelity, aligning quantization directly with the functional objectives of VLA models. To facilitate hardware-friee friendll implementation, we adopt uniform-bit bit activations across the model, a pragmatic choice that avoids the runtime branching and kernel fragmentation that can degrade computational performance. In contrast, weights receive more fine-grained, per-output-hhnnel integer quantization. To enable this, all operators are first standardized as linear maps of the form Y XW + b, where convolutions equivalent linear operators. Each output channel can then be assigned a unique bit-width from {0, 2, 4, 8, 16). formulation that elegantly unifies quantization with structural pruning by treating a 0-bit assignment as a channel to be pruned. Finally, performance is evaluated directly within the action space measure true task-level impact. More specifically, single-step accuracy quantified using Action-MSE under a teacher- teacher-forcing paradigm, while temporal robustness is assessed through short-horizon rollouts by measuring cumulative action and end-effector deviations alongside final task success rates. Fig. 2 shows our overall framework.
treated as their
3.3.1 ESTIMATION OF AETTON-SPACCE SENSITIVITY
Our bit allocation strategy is guided by the action-space sensitivity metric defined in Eq. and 5 For each channel (1, c), we compute set of sensitivity scores, & by evaluating its impact
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Step
1 Sensitivity Analysis
wegebabeee. 2.
senditivity matrix
precision action
dan
Llama 2 7B
VLA
quontized
(full precision)
suns liviyy
thoirele
VS
fon
Tokenizer
Projector
ViT
Couantized bit)
full precision
"Pick up yellow may"
weight matrixx
Otheee qpont bed bey)
from Base VLA
order by
sensitivity
Step Assign Gates
oxsign lower bit
longuage instruction
sonsitivity
observation
Base VLA
Quentized
Presision
VLA
Idem 2ut dar D61
VLA
chornel number
Traverse
Action
quantizze to ossigned bit
sensitivity order
quatt aed weight matrix
full precision weight matrix
Figure 2: The pipeline of our QVLA framework consists of two steps: (i) In Step 1, we conduct a fine-grain fine-grained action sensitivity analysis by systematically measuring and ranking the error induced by quantizing each channel to various bit-widths. (ii) Step 2, an optimal bit-width is assigned to each channel using greedy demotion algorithm, which iteratively prunes or lowers the precision of the least sensitive channels until the target bit budget is met.
different bit-widths b E 20, 8, 16} on calibration set D. A crucial property of these scores is their inherent comparability across all network components (modules, layers, and channels), which enables them to serve as the primary signal for our global bit allocation algorithm. To ensure this single-step metric is also a valid proxy for performance in dynamic control tasks, we introduce cumulative variant, s designed to capture short-horizon error accumulation. In practice, we find
that the rankings of channel sensitivities produced by $1.e and s are highly consistent. This crucial finding allows us leverage the computationally cheaper single-step metric (b) for guiding the bit
allocation process, while using the more comprchensive cumulative metric s/ to validate that our approach successfully extrapolates to long-horizon performance.
While the sensitivity metric $i,c provides a robust signal for bit allocation, exhaustively comput- ing for every channel and bit-width computationally prohibitive. To overcome this challenge, we introduce an efficient two-stage strategy that combines a rapid, approximate screening with a targeted, precise evaluation. First, we derive a first-order approximation to serve as proxy for sen- sitivity. The core idea is to model the local relationship between channel's output, Xt,e- and the final action, A, using a linear approximation based on the Taylor expansion. AX, at the channel output will induce a deviation in the action, AA, which can be approximated as AA JAX,,, AX,,e- To quantify the magnitude this effect, we consider the vector norms,
(b)
small perturbation
| = ||JA......l ||A
where JA.Xsw., is the Jacobian of the action with respect to the channel output. In this formula- tion, the matrix norm [|JA.x,..ll serves local sensitivity gain, scalar value that quantifies how much a perturbation's 's magnitude is amplified as it propagates to the action space. The perturbation induced by quantization is modeled the quantization error, AXte (Q(W,) Wi)X,, This allows us to compute a rapid importance score, i.e.. the product of the Jacobian gain and the esti- mated quantization noise, to create global ranking of all channels. Then, based on this ranking, we perform limited number of full forward passes, selectively targeting the most important channels to precisely calibrate their true sensitivity scores. This hybrid approach allows us focus computa- tional effort on preserving sensitive interfaces, such as the projector and action head, while enabling aggressive compression of less critical channels.
3.3.2 OPTIMAL BIT ALLOCATION UNDER CONSTRAINED BUDGET
With the sensitivity scores for each potential bit-width established, we can now formulate the overall bit allocation task constrained optimization problem. Specifically, we aim assign a bit-width

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Published as a conference paper at ICLR 2026
Table I: Performances under various weight-activation quantization settings. W4A4/W8A8 refers to the quantization of weights (W) and activations (A) to 4 and 8 bits, respectively. Note that since our method assigns bits adaptively on a per-channel basis, we report the average bit-width.
Model
Setting
Method
Spatial
Object
Goal
Long
Mem. (GB)
Speedup
FP Model
84,7%
88.4%
79.2%
53.7%
76.5%
15.2
1x
SmoothQuant
84.2%
87.8%
77.8%
53.2%
75.8%
-0.7%
7.4
1.40x
W8A8
OuniQuant
82.6%
86.2%
74.8%
51.7%
73.8%
-2.7%
1.26x
OLe
QVLA
85,2%
88.0%
77.6%
54.2%
76.3%
-0.2%
7.1
1.42x
SmoathQuant
69.2%
73.2%
69.6% 6%
40.9%
63.2%
-13,3%
4.7
1.52x
W4A4
OmniQuant
82.2%
85.4%
75.4%
50.3%
73.3%
-3,2%
5,4
1.4x
QVLA
84,4%
87.6%
78,88 8%
53.0%
76.0%
-0.5%
4.3
1.47x
FP Model
97.6%
98.45
97.9% 9%%
94.5%
97.1%%
15.4
1x
SmoathQuant
96.0%
Ope -OFT
W8A8
OmniQuant
95.4%
96.2%
93.0%
92.6%
94.3%
-2.8%
8.0
1.30%
QVLA
97.2%
25%
95.8%
94.3%
96.4%
0.7%%
7.2
1.36x
SmoothQuant
77.2%
70.0%
77.8%
73.4%
-23.7%
4,9
1.53x
W4A4
OmniQuant
95.0%
94.4%
94.0%
92.0%
93.9%
-3.2%
5.7
1.37%
QVLA
96.2%
97.6%%
96.4%
93.8%
96.0%
-1.1%
4.5
1.49x
bi. (0, 2,, to each channel to minimize the action error, subject an average budget B:
min
Σ 1,0
$1.
(b
s.t.
Σ 1,c
br.c
B,
[br.c]
N
where is the total number of channels and O-bit signifies pruning. To solve this NP-hard problem efficiently, we propose a greedy demotion algorithm. The procedure begins by initializing all chan- nels to the highest precision, 16-bit. then proceeds series of demotion stages (16 - 8, 8 -> 4, 4 + 2, and - 0) until the average bit budget B is met. Within each stage, from a higher bit-width bui to a lower one bj. we evaluate the cost-fffectiveness demoting each candidate channel using the sensitivity-te -t-biitttttttttttt ratio Pi.ci
(bm) $1e
(bin)
Pi.c
bn
bio
This ratio represents the marginal increase in error for each bit saved. To make the most efficient bit reductions, we prioritize demoting channels that are least sensitive to quantization, i.e., those with the lowest Pt.c values. Specifically, in the 16 + 8 stage, we sort all channels by their corresponding Pt.c in ascending order and sequentially demote them to 8-bit. After each demotion, we check the total bit budget. If the budget met, the process stops. Otherwise, the algorithm proceeds to the 8 - 4 stage, repeating the sort-and-demote process for all channels currently 8-bit, and for the remaining stages. To further refine the final bit assignment, we introduce several heuristics. To mitigate over-pruning, the final + 0 demotion stage is regularized using dual-threshold and LO-style constraints. The computational complexity of this method dominated by sorting, scaling ( log C),, where C is the total number of channels.
Activations are assigned a uniform bit-width (e.g., 8-bit) using distribution-awant calibration, which ensures a branch-free execution path and stable latency. Weights stored a per-row basis, each with its own scale and zero-point, and are dequantized upon access. This approach avoids per-channel runtime branching. The scheme applies to all linear and convolutional layers in both the vision and language backbones. See Appendix for more detailed pseudocode.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS AND DETAILS
To evaluate our method, we adopt the widely used LIBERO benchmark (Liu et al. 2024a), compris- ing four distinct task suites for robot manipulation. Our floating kooting-point (FP) baseline employs models with weights in the BF16 format, and all experiments were conducted on NVIDIA RTX 4090 GPUs. In quantization strategy, we selectively apply channel-wise weight quantization with gated bit
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Table Performances under various weight-only quantization settings. Weight-only quantiza- tion primarily targets the reduction of memory, typically offering marginal improvement in latency.
Model
Setting
Method
Spatial
Object
Goal
Long
Avg. f
A
Mem. (GB) 4
FP Model
84.7%
88.4%
79.2%
53.7%
76.5%
15.2
AWQ QVLA
82.3%
87.2%
74.6%
50.7%
73.7%
-1.8%
7.6
OLenVLA
W8A16
86.2%
88.4%
79.4%
53.1%
76.8%
+0.3%
7.2
AWQ QVLA
80.0%
81.2%
74.6%
47.2%
70.8%
-4.7%
5.0
W4A16
86.0%
88.6%
78.4%
52.8%
76.5%
+0.0%
4.3
FP Model
97.6%
98.4%
97.9%
94.5%
97.1%
15.4
AWQ QVLA
95.2%
96.8%
95.4%
93.1%
95.1 1%
-2.0%
8.0
OpenVLA-OFT
W8A16
97.4%
98.6%
97.2%
94.6%
97.0%
-0.1%
7.4
AWQ
93.0%
92.4%
93.8%
90.7%
92.5%
-4.5%
5.2
W4A16
QVLA
97.0%
98.4%
96.8%
94.4%
96.7%
-0.4%
allocation to the vision backbone and language module. The projector and action head remain in full BF16 precision to preserve control stability. The core of our method is an action-centric sensi- tivity analysis, which requires a calibration set sampled from LIBERO training demonstrations and augmented with a small instruction-on
subset. Using this set, we measure the sensitivity score (Eq. [) for each channel in the target modules by simulating its quantization to various bit-widths b E (0, 2, 4, 8, 16). To confirm the validity of this metric, the resulting sensitivity rankings are further cross-vale validated with short environmental rollouts. While our diagnostic analysis spans all modules, the final average-bit budget and the effects of pruning (0-bit quantization) are computed exclusively over the layers designated for quantization, ensuring fair comparison.
4.2 COMPARISON WITH STATE-OF-THE-ARTY
Results on weight- weight-activation quantization. Tab. B compares the performance of our QVLA with SmoothQuant Xiao al. (2022). OmniQuant (Sao et al. (222)) two methods initially designed for L.Ms//LLLMs, under weight-activation quantization. The results demonstrate that QVLA achieves a superior trade-off between high task success rates, low memory footprint, and fast inference speed. For instance, under the W4A4 quantization setting for the OpenVLA model, QVLA retains 99.3% of the full-precision performance, incurring minimal accuracy drop of only 0.5%. This is sub- stantially smaller than the 13.3% drop from SmoothQuant and the 3.2% from OmniQuant. This comprehensive advantage is achieved while requiring only 28.2% the original model's memory and delivering 1.47x inference speedup. These results validate the effectiveness of our action- centric quantization strategy, establishing QVLA as a compelling choice for balancing performance and efficiency in resource-constrained environments.
Results on weight-only quantization. The performance comparison under weight-only quantiza- tion, presented Tab. 2 reveals the consistent superiority our QVLA. This advantage is partic- ularly pronounced in the most challenging W4A1 16 setting. Specifically, on the OpenVI.A model, QVLA incurred zero performance loss, whereas AWQ's [in et al. (2023) average success rate dropped by 4.7%. Similarly, for the Open VLA-OFT model, QVLA's performance degradation was minimal at just 0.1 % and 0.4%, markedly lower than the 2.0% and 4.5% losses observed with AWQ. Collectively, these results robustly demonstrate QVLA's superior ability preserve model ac- curacy under aggressive compression.
4.3 FURTHER ANALYSIS
The superiority of channel-wise quantization. We conducted an ablation study with Open-
VLA Kim et al. (0024) to validate our choice of channel-wise quantization over the conventional layer-wise method for the VLA model. As detailed in Tab. B our approach demonstrates a dis- tinct advantage when benchmarked against the 76.5% success rate of the full-precision baseline on LIBERO. At INT4 precision, the channel-wise method perfectly preserves the bascline perfor- mance (76.5%), whereas its layer-wise counterpart suffers notable decline 74.8%. This superi-
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Published as a conference paper at ICLR 2026
Table 3: Comparison of layer-wise and channel-wise quantization methods on various tasks. The baseline is OpenVLA.
Cumulaaive MSE
Reduied bii- wilth
Method
macreasess
Weight Type
Spatial
Object
Goal
Long
Avg. 1
Layer
Channel
FP Model
84.7%
88.4%
79.2%
33.7%
76.5%
V
84.2%
86.8%
77.0%
51.2%
74.8%
INT4
86.0%
88.6%
78.4%
52.8%
76.5%
34
Timestep
INT8
84.0%
87.2%
77.8%
50.6%
74.9%
Figure 3: Quantification of temporal error accumulation.
86.2%
88.4%
79.4%
53.1%
76.8%
Table The influence of pruning (0-bit quantization) and uniform-bit quantization under an overall INT8 budget. The row @ with "prune" and without "uniform-bit indicates our proposed quantization method with the (0, 2, 4, 8, 16) as candidates during assigning bit to each channel.
Quantization Method
Weight Type
Spatial
Object
Goal
Long
Avg. 1 Memory (GB)
Prune
Uniform Bit
FP Model
84.7%
88.4%
79.2%
53.7%
76.5%
15.2
86.4%
88.0%
79.0%
53.4%
76.7%
7.5
83.6%
87.0%
77.4%
50.2%
74.6%
7.6
INT8
86.2%
88.4%
79.4%
53.1%
76.8%
84.0%
87.2%
77.0%
50.4%
74.7%
7.1
ority even more pronounced at INT8 precision, where our method not only matches but surpasses the baseline 76.8%. In stark contrast, the layer- approach again degrades performance, falling to 74.9% 4... This study provides compelling evidence that channel- -wiee quantization is the superior strategy for compressing our VI.A model.
Mitigating temporal error accumulation. Fig. iillustrtte the temporal accumulation of action errors. As expected, cumulative emror increases over time for all quantization methods, with 4-bit quantization showing a significantly faster rate of error growth than 8-bit methods. Crucially, our proposed 8-bit method consistently maintains lower cumulative line. This performance gap widens progressively over the time horizon, highlighting our method's superior ability to mitigate long-horizon error propagation. This sustained reduction error demon- strates the enhanced stability and long-horizon robustness conferred by our approach, an advantage that becomes particularly pronounced as dynamic effects accumulate over longer episodes.
than the uniform 8-bit base-
The impact of prune and uniform bit. shown in Tab channel-wise gating with (2, 4, 8. 16) bits as candidates for each channel's quantization already matches the full-precision (FP) baseline performance (Ø 76.7% vs. ① 76.5%), confirming the effectiveness of concentrating bits critical channels. When combined with pruning (from (2, 4, 8, 16) to @(00 2, 4, 8, 16)), this method fur- ther reduces memory to 7.0 GB while also slightly boosting the average success rate to 76.8%. In contrast, enforcing a uniform bit-width @ only 8-bit) causes a substantial drop in performance to 74.6%. Subsequent pruning (0, 83) is unable to recover this loss, yielding only 74.7%. This indi- cates that a uniform quantization strategy is fundamentally ill-suited for the VIA model. Therefore, under overall INT8 budget, the combination of channel-wise gating with pruning (0-bit) proves to be the superior strategy, achieving the highest accuracy while minimizing the memory footprint.
4.4 PERFORMANCE ON REAL-WORLD TASKS
Real-world Setup. We construct bimanual robotic system to perform real-world manipulation tasks. Two IMETA-YI robotic arms are employed for execution. The system utilizes three Orbbec DaBai DCW2 cameras to capture visual observations: two mounted the wrists to provide egocentric views, and one fixed externally to capture the global third-person view. The real-world robotic system is shown in Fig.

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Published as a conference paper at ICLR 2026
Vien Causers (Orblee Pubai DCW2)
OnesAnn
Graper Laagge Eoe llee Datsi wal
Pick chups
pen
Daal Arm Tark
DIEEL-V
(a) Real-world System Sctup
(b) Real-world Task examples
Figure 4: Real-world system IMETA-Y Y1 and the task examples. The system consists of two IMETA-
Y1 robotic arms and three Orbbec DaBai DCW2 cameras.
Table 5: Real-world success rate comparison with QVLA.
One-Arm Task
Dual-Arm Task
Method
Setting
Pick white pen
Pick potato chips
Fold towels
Average
SpeedUp
8/10
7/10
4/10
63.3%
1.00x
QVLA
W8A16
8/10
6/10
5/10
63.3%
1.28x
Tasks and Datasets. Our dataset cover both single-arm and dual-arm tasks, well as simple and complex dexterous tasks. Simple tasks are short-horizon operations, such as placing white pen into a pen holder or grasping and transferring potato chips into a bin. Dexterous tasks require continuous and sustained control, such as folding towel which typically involve fine-grained manipulation of deformable objects. We collected fifty trajectories for each single-arm task and one hundred trajectories for the dual-arm tasks, all sampled at frequency of 30Hz.
Results. We use the To model as real-world bascline and performed quantization under the W8A16 setting. Tab. preesetss the results of the real-world experiments. We observe that QVLA performs excellently both single-arm and dual-arm tasks, with performance essentially par with the original model. This strongly demonstrates the robustness of our quantization method, which maintains good performance even in real-world environments. Furthermore, achieved an acceleration ratio of 1.28x x under the W8A16 16 setting, Our model is trained on NVIDIA A100 GPUs and deployed for inference on single NVIDIA 4070 GPU.
For additional technical depth, please refer to the Appendix, including the rigorous theoretical derivation sensitivity proxy and the complete algorithmic details. The Appendix also presents generalization results on the UniVLA architecture and CALVIN benchmark, alongside qualitative policy visualizations and ablation studies that further substantiate the robustness and architectural transferability of our framework.
CONCLUSION
This paper presents the first systematic analysis of quantization challenges in VLA models. We demonstrate that naively migrating quantization strategies from other domains, such as the uniform- bit methods common for LLMM//MLLMs. is ill-suited for these action-driven models and leads to significant performance degradation. This failure stems from the VLA's unique sensitivity noise, a critical barrier to their deployment on resource-constrained robots. To address this gap. we propose QVLA, adaptive quantization framework specifically designed for VLAs. Our action-centric ap- proach reframes the problem by anchoring optimization objectives in the action space. It unifies quantization and pruning via a per-channel sensitivity metric and a global greedy algorithm. Ex- tensive evaluations on VLA and OpenVLA--OFT baselines confirm that QVLA outperforms conventional methods, reducing action errors and enhancing task success rates to a level that can surpass the full-precision bascline. Looking ahead, spectrum VLA architectures. This further validate our framework's generalizability and solidify its role key enabler for deploying capable foundation models real-world robotic systems.
going to adapt QVLA to a broader
Acknowledgement. This work was supported in part by the Natural Science Foundation China (Grant 6o.65200332, 62372451, 62192785, 62372082). the Beijing Natural Science Foundation (JQ24022), CAAI-Ant Group Research Fund (CAAI-MYJJ 2024-02), Young Elite Scientists Spon- sorship Program by CAST (2024QNRC001).
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REFERENCES
Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L Croci, Bo Li, Pashmina Cameron, Martin Jaggi. Dan Alistarh, Torsten Hoefler, and James Hensman. Quarot: Outlier-free 4-bit inference in rotated llms. arXiv preprint 24000:240 2024. NeurIPS 2024 (poster).
Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al. To: A vision- Aasion-language-attinn flow model for general robot control. arXiv preprint 2xXi:22400224144 2024.
Qingwen Bu, Yanting Yang. Jisong Cai, Shenyuan Gao, Guanghui Ren, Maoqing Yao, Ping Luo, and Hongyang Li. Univla: Learning to act anywhere with task-centric latent actions. arXiv preprint arXiv:2505.06111, 2025.
Zhen Dong, Zhewei Yao, Amir Gholami, Michael W Mahoney, and Kurt Keutzer. Hawq: Hessian aware quantization of neural networks with preeedprecision. ICCV, 2019.
Zhen Dong, Zhewei Yao, Yaohui Cai, Daiyaan Arfeen, Amir Gholami, Michael Mahoney, and Kurt Keutzer. Hawq-v2: Hessian aware trace-weighted quantization of neural networks. In NeurlPS, 2020.
Steven K. Esser, Jeffrey L McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmen- dra Modha. Learned step size quantization. ICLR, 2020. 1rXiv:1902.08153.
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. Gptq: Accurate post-training quantization for generative pre-trained transformers. arXiv preprint arXiv:2210. 17323, 2022.
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. Quantization and training of neural networks for inference. In CVPR, 2018.
efficient
Feng Jiang, Zihao Zheng, Xiuping Cui, Maoliang Li, Jaayu Chen, and Xiang Chen. Eaqvla: sacooding-aligned quantization for ission-laguuage-cttonn models, 2025. URL https://
arxiv. or.oorggaaa abs 2505. 21567
Moo Jin Kim, Chelsea Finn, and Percy Liang. Fine-tuning vision- Larioon-nnguage-acee actionn models: Opti- mizing speed and success, 2025. URL https://rxiiv. org/abw/2502
Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, Quan Vuong, Thomas Kollar, Ben- jamin Burchfiel, Russ Tedrake, Dorsa Sadigh, Sergey Levine, Percy Liang, and Chelsea Finn. Openvla: An open-source vision-languuge-attion model. arXiv preprint arXiv:2406.09226. 2024.
Yuhang Li, Ruihao Gong. Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu. Breeq; Pushing the limit of post-training quantization by block reconstruction. arXiv preprint arXiv: 102.0022222222 2021.
Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, and Song Han. Awq: Activation-aware weight quantization for IIm compression and acceleration. arXiv preprint arXiv:2306.00977, 2023. MLSys 2024 (best paper), v5 (2024-07).
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, 2024a.
Songming Liu, Lingxuan Wu, Bangguo Li, Hengkai Tan, Huayu Chen, Zhengyi Wang, Ke Xu, Hang Su, and Jun Zhu. Rdt- lb; diffusion foundation model for bimanual manipulation. arXiv preprint arXiv:2410.0788 2024b.
Yufan Liu, Jiajiong Cao, Bing Li. Chunfeng Yuan, Weiming Hu, Yangxi Li, and Yunqiang Duan. Knowledge distillation via instance relationship graph. In Proceedings of the IEEE/CVF confer- on computer vision and pattern recognition, pp. 7096-7104, 2019.

---

Published as a conference paper at ICLR 2026
Yufan Liu, Jiajiong Cao, Bing Li, Weiming Hu, Jingting Ding, and Liang Li. Cross- knowledge distillation. In Proceedings of the Asian conference on computer vision, pp. 3396- 2022a.
Yufan Liu, Jiajiong Cao, Bing Li, Weiming Hu, and Stephen Maybank. Learning explore distill- ability and sparsability; joint framework for model compression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3)3337-3333 -3395, 2022b.
Zechun Liu, Changsheng Zhao, Igor Fedorov, Bilge Soran, Dhruv Choudhary, Raghuraman Krish- namoorthi, Vikas Chandra, Yuandong Tian, and Tijmen Blankevoort. Spinquant: Lim quantiza- tion with learned rotations. arXiv preprint arXiv:2405. 16406 2024c.
Markus Nagel, Mart van Baalen, Tijmen Blankevoort, and Max Welling. Up or down? adaptive rounding for post-training quantization. arXiv preprint arXiv:2004. 10568, 2020.
Octo Model Team, Dibya Ghosh. Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari,Joey Hejna, Charles Xu, Jianlan Luo. Tobias Kreiman, You Liang Tan, Lawrence Yunliang Chen, Pannag Sanketi, Quan Vuong. Ted Xiao, Dorsa Sadigh, Chelsea Finn, and Sergey Levine. Octo: An open-source generalist robot policy, In Proceedings of Robotics: Science and Systems, Delft, Netherlands, 2024.
Seongmin Park, Hyungmin Kim, Wonseok Jeon, Juyoung Yang, Byeongwook Jeon, Yoonseon Oh, and Jungwook Choi. Quantization-awarr imitation-leariing for resource-efficient robotic control, 2024. /ttpps///aax....o////aasaas
Xiaofeng Ruan, Yufan Liu, Chunfeng Yuan, Bing Li, Weiming Hu, Yangxi Li, and Stephen May- bank. Edp: An efficient decomposition and pruning scheme for convolutional neural network compression. IEEE Transactions on Neural Networks and Learning Systems, 32(10):4499-43 4513, 2020.
Xiaofeng Ruan, Yufan Liu, Bing Li, Chunfeng Yuan, and Weiming Hu, Dpfps: Dynamic and progressive filter pruning for compressing convolutional neural networks from scratch. In Pro- ceedings of the AAAI Conference on Artificial Intelligence, volume 35, pp. 2495-25900 2021.
Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu, Lirui Zhao, Zhiqian Li, Kaipeng Zhang, Peng Gao, Yu Qiao, and Ping Luo. Omniquant: Ommidireectionally calibrated quantization for large language models, 2024. URL https://arxiv wtwpevvvorr/abbb////eea 13137
Mustafa Shukor, Dana Aubakirova, Francesco Capuano, Pepijn Kooijmans, Steven Palma, Adil Zouitine, Michel Aractingi, Caroline Pascal, Martino Russi, Andres Marafioti, et al. Smolvla: A vision- vision-language- language-action model for affordable and efficient robotics. arXiv preprint arXiv:2506.01844 2025.
Wenxuan Song, Jiayi Chen, Pengxiang Ding, Yuxin Huang, Han Zhao, Donglin Wang, and Haoang Li. Ceed-vla: Consistency vision- languag-aaato model with early-exit decoding. arXiv preprint 1372125506..375,, 2025a.
Wenxuan Song, Jiayi Chen, Pengxiang Ding, Han Zhao, Wei Zhao, Zhide Zhong, Zongyuan Ge, Jun Ma, and Haoang Li. Accelerating vision-language-actioo model integrated with action chunking via parallel decoding. arXiv preprint arXhv:2503.023100 2025b.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Ar- mand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation
language models, 2023a. URL /ttp://///arxvv.orrr//////222222 aaaaab///32.1390 13971
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaci, Niko- lay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open founda- tion and fine-tuned chat models. arXiv preprint #=Xiv:2307.09288 2023b.
Changyuan Wang. Ziwei Wang, Xiuwei Xu, Yansong Tang, Jie Zhou, and Jiwen Lu. Q-vim: Post- training quantization for large dision-languee models. Advances in Neural Information Process- ing Systems, 37:1 14553-1 14573, 2024.
12

---

Published as a conference paper at ICLR 2026
Junjie Wen, Yichen Zhu, Jinming Li, Minjie Zhu, Kun Wu, Zhiyuan Xu, Ning Liu, Ran Cheng, Chaomin Shen, Yaxin Peng, et al. Tinyvla: Towards fast, data-efficient ssion-languge-aattin models for robotic manipulation. arXiv preprint 120.24400.12514, 2024.
Guangxuan Xiao, Ji Lin, Mickaël Seznec, Hao Wu, Julien Demouth, and Song Han. Smoothquant: Accurate and efficient post-training quantization for large language models. arXiv preprint arXiv:2211.10438, 2022. ICML 2023.
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. 10100, 2025.
Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, al. Rt-2: Vision-language-action models transfer web knowledge to robotic control. In Conference on Robot Learning, pp. 2165-2183, PMLR, 2023.
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Reviews:
[{"review_id": "vs3s9YosKJ", "reviewer": "Reviewer_kUNf", "summary": "This paper presents AutoQVLA, a novel action-centric quantization framework for embodied control. They allocate different bitwidths for different channels based on a per-channel action sensitivity. To overcome the computational challenge of computing the sensitivity metric for all channels and bit-widths, they first derive a linear approximation of the channel amplification factor using Taylor expansion and then do a fine-grained measurement. Then they propose a greedy algorithm to assign bit-widths to different channels.", "questions": "Please see the weaknesses I mentioned.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The algorithm defined in the paper is novel way of quantizing Video Language Action models\n2. They efficiently allocate the computation budget for identifying the sensitivity of different channels\n3. They empirically show that their algorithm achieves performance close to the full precision model, while reducing the VRAM required and  achieving speedup", "weaknesses": "1. The evaluation is focused on one class of models and a single benchmark; it is not clear how the method generalizes across models and benchmarks\n2. No detailed ablation studies highlighting the contribution of different parts of the method, like the calibration set sizes, gate ratio selection etc.,\n3. No theoretical analysis of the choices made in the paper", "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": "NqIq5RuiYg", "reviewer": "Reviewer_9cSM", "summary": "This paper introduces AutoQVLA, a specialized framework for quantizing Vision-Language-Action (VLA) models. The framework addresses the limitations of traditional quantization methods that struggle to maintain output precision in VLA models. It proposes an action-space-based sensitivity quantization approach. The core of AutoQVLA lies in its fine-grained, per-channel bit allocation strategy, which measures each channel’s sensitivity to the final action output during quantization. Unlike conventional uniform-bit quantization methods, AutoQVLA introduces adaptive per-channel precision adjustment to optimize quantization efficiency while ensuring robustness in action generation.", "questions": "Further questions:\n\n1. According to the analysis in SmoothQuant and OmniQuant, quantizing activations to 4 bits (A4) is significantly more challenging than quantizing weights to 4 bits (W4). For instance, OmniQuant reports approximately a 10% performance drop under A4 quantization (see Table 2 in OmniQuant [1]). In contrast, the method proposed in this paper appears to focus exclusively on weight quantization and does not introduce any specialized treatment for activation quantization. Surprisingly, the reported results show negligible performance degradation even under aggressive A4 quantization. This discrepancy raises serious concerns about the validity of the experimental claims. To ensure reproducibility and credibility, the authors should release both the training code and the quantized pretrained models, and provide a detailed ablation study addressing why their method seemingly avoids the well-documented pitfalls of low-bit activation quantization. Without such evidence, the reported results remain unconvincing.\n\n2. To the best of my knowledge, data types such as 2-bit and 4-bit are not natively supported in mainstream frameworks like PyTorch. As discussed by prior works like AWQ, achieving actual memory savings from low-bit quantization typically requires custom CUDA kernels design. It is therefore unclear whether the memory reduction reported in this paper reflects real hardware-level savings or is merely a theoretical estimate based on idealized bit-width calculations. \n\nMoreover, in the W4A4 setting described in the paper, activations and weights may be quantized to heterogeneous bit-widths (e.g., 2-bit, 8-bit), and the paper implies that they are first upcast or aligned to 4-bit before matrix multiplication. Such a process would inevitably introduce computational overhead and degrade inference speed. Yet the paper claims improved inference throughput—how was this speedup measured? Did the authors implement a custom CUDA kernel specifically optimized for mixed-precision W4A4 matrix multiplication? Without clarification on these implementation-level details and empirical profiling results (e.g., latency, memory usage on actual hardware), the efficiency claims lack substantiation.\n\n3. In your paper, you mention that the sensitivity is calculated through the error in the action space. Could you clarify whether this metric is strongly dependent on specific tasks or data distributions? When transitioning to new tasks or environments, is it necessary to recalibrate or adjust the way this metric is calculated?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.The paper introduces a fine-grained approach to quantifying errors by isolating individual channels, allowing for a more precise evaluation of the error for each channel at different bit-widths, ensuring better control over the quantization process.\n2.Unlike traditional global bit allocation, the paper proposes a per-channel adaptive bit allocation strategy and employs a greedy search algorithm to optimize bit allocation. The algorithm dynamically adjusts the bit-width for each channel based on its sensitivity, effectively balancing performance and computational efficiency.", "weaknesses": "1. The sensitivity evaluation and bit allocation are only conducted on a few VLA models in the paper. It remains unclear whether the same process needs to be applied to other VLA models, and if so, whether it will lead to significantly higher computational resource consumption. A more generalizable analysis across a broader range of VLA models such as UniVLA[1] would provide better insight into the scalability of the proposed method.\n2. Inadequate comparison with relevant baselines. Since the VLA architecture is structurally most similar to Vision-Language Models (VLMs), it is essential to compare against VLM-specific quantization methods, such as VLMQ [2] and Q-VLM [3], rather than unimodal LLM quantization approaches. A direct comparison with these VLM quantization baselines would provide a more meaningful assessment of the proposed method’s effectiveness in the multimodal setting.\n3. Limited Novelty. The proposed channel-wise mixed-precision quantization scheme lacks significant novelty. Similar channel-wise quantization strategies have already been explored in prior work, notably in GPTQ [4]. Moreover, the importance-aware quantization design is also conceptually aligned with discussions in GPTQ.\n\nMinor Weaknesses:\n\n4.The pipeline diagram could be clearer. While it outlines the workflow of the method, its current presentation may not be intuitive or detailed enough for readers to fully grasp the process. Improving the clarity and labels in the diagram would enhance understanding, especially when explaining the interactions between different steps in the quantization process.\n\n[1]Bu, Qingwen, et al. \"Univla: Learning to act anywhere with task-centric latent actions.\" arXiv preprint arXiv:2505.06111 (2025).\n\n[2]Xue, Yufei, et al. \"Vlmq: Efficient post-training quantization for large vision-language models via hessian augmentation.\" arXiv preprint arXiv:2508.03351 (2025).\n\n[3]Wang, Changyuan, et al. \"Q-vlm: Post-training quantization for large vision-language models.\" Advances in Neural Information Processing Systems 37 (2024): 114553-114573.\n\n[4]Frantar, Elias, et al. \"Gptq: Accurate post-training quantization for generative pre-trained transformers.\" arXiv preprint arXiv:2210.17323 (2022).", "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": "t8nYzb0oNV", "reviewer": "Reviewer_2rUj", "summary": "This paper presents AutoQVLA, a novel quantization framework specifically designed for VLAs. The key insight is that naively applying uniform quantization methods from LLMs/MLLMs is suboptimal for action-driven models, as minor action deviations can compound into catastrophic failures in embodied control. This paper introduces a highly granular, channel-wise bit allocation strategy to quantize each channel to various bit-widths. Comprehensive experiments show that AutoQVLA maintains 98.9% of original performance while using only 29.2% of VRAM and achieving 1.49$\\times$ speedup compared with the original OpenVLA-OFT model.", "questions": "Would the presence of greater noise in real-world observations affect the sensitivity of channel parameters to quantization errors and action generation?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Conducted a detailed analysis of the sensitivity of parameters across different channels to action generation;\n2. Significant memory reduction and speedup with minimal performance loss make large VLA deployment feasible.", "weaknesses": "All experiments are conducted on LIBERO benchmarks, where there is a lack of research on model quantization in real-world tasks.", "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": "laOB2hErTb", "reviewer": "Reviewer_RtD1", "summary": "The paper proposes AutoQVLA, a VLA quantization method that reduces memory requirements and inference latency of VLAs by employing channel-wise bit allocation and pruning which optimize for reduced degradation of action quality. The authors show that naive uniform-bit quantization methods used for LLMs underperforms when applied to VLAs since they do not take into account how errors compound across trajectories when the VLAs are deployed as policies. The authors propose a more sophisticated framework that first analyzes the action sensitivity of different channels within network layers and then runs a greedy demotion algorithm to iteratively demote channels from full precision to lower precision until a target bit budget is met. Experimental results with OpenVLA and OpenVLA-OFT policies in the LIBERO simulation benchmark demonstrate nearly 4x VRAM reduction and 1.5x faster inference speed, with only up to 1% absolute drop in average success rate, outperforming prior quantization methods.", "questions": "See questions in Weaknesses section.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "* The paper is well motivated and generally easy to follow.\n* The paper achieves significant reduction in memory requirements (about 4x) and improvement in inference latency (about 1.5x) while preserving most of the accuracy of full-precision VLAs. These efficiency results surpass those of prior methods (SmoothQuant, OmniQuant, AWQ), and the degradation in policy performance is substantially lower. This suggests that the method can be practically useful for low-resource deployment of VLA policies.\n* There is good breadth in the experiments: The analyses cover weights-and-activations and only-weights quantization, layer- vs channel-wise quantization, temporal error accumulation across different quantization schemes, and 0-bit (\"pruning\") and uniform-bit quantization.\n* The analysis of quantization is performed on two VLA instances that vary substantially in their action generation schemes (OpenVLA which does vanilla autoregressive decoding of tokens and OpenVLA-OFT which does parallel decoding of continuous actions). Findings hold across both instances and overall support the authors' claims.", "weaknesses": "Major:\n* The experiments are limited to one benchmark of robot tasks in simulation (LIBERO). Results in another simulator (e.g. RoboCasa or CALVIN) or real-world robotic manipulation tasks would further strengthen the paper.\n* The temporal error accumulation analysis in Section 4.3 and Figure 3 could benefit from greater detail. It is unclear under which evaluation setting the curves in the plot are computed (e.g. what is the task here, what is the policy being analyzed?). In addition, some discussion on how the differences in cumulative MSE affect the episode outcomes can be helpful (e.g. what do these differences mean in terms of success rate)?\n* Qualitative analysis of how the AutoQVLA-quantized VLA policies behave in comparison to the full-precision policies or the policies quantized using prior methods is missing and can further strengthen the paper. I.e., beyond success rate differences, how do the AutoQVLA policies actually behave when rolled out as policies, and how do they succeed/fail? Are there example rollouts that can illustrate why the other quantization approaches are less effective?\n* In Section 3.3.1, the authors write, \"In practice, we find that the rankings of channel sensitivities produced by $s_{l,c}^{(b)}$ and $S_{l,c}^{(b)}$ are highly consistent. This crucial finding allows us to leverage the computationally cheaper single-step metric $s_{l,c}^{(b)}$ for guiding the bit allocation process, while using the more comprehensive cumulative metric $S_{l,c}^{(b)}$ to validate that our approach successfully extrapolates to long-horizon performance.\" This finding is important, yet evidence supporting it is missing. Details on how this conclusion was made would be helpful.\n* There is lack of detail on the calibration set used for LIBERO and how it is curated.\n* It is unclear how many trials/random seeds are used for the evaluations (e.g. Table 1 and 2).\n\nMinor:\n* Figure 1(b): The separation of vision encoder, projector, language model, and action head is not very clear. For example, where does the language model section start exactly in this plot?\n* Figure 2 step 1: \"tagrt bit\" typo?\n* Table 4: \"The row (3)\" typo? Shouldn't it be \"The row (4)\" instead?", "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)