FASTer: Toward Powerful and Efficient Autoregressive Vision–Language–Action Models with Learnable Action Tokenizer and Block-wise Decoding
Authors: Yicheng Liu
Shiduo Zhang
Zibin Dong
Baijun Ye
Tianyuan Yuan
Xiaopeng Yu
Linqi Yin
Chenhao Lu
Junhao Shi
Luca Jiang-Tao Yu
Liangtao Zheng
Jingjing Gong
Tao Jiang
Xipeng Qiu
Hang Zhao
Venue: ICLR 2026 Poster (2026)
Keywords: VLA
embodied AI
robotics
Primary area: applications to robotics, autonomy, planning
TL;DR: We proposed a novel action tokenization method and powerup the autoregressive VLA, both in performance and inference speed.

Abstract: Autoregressive vision-language-action (VLA) models have recently demonstrated strong capabilities in robotic manipulation. However, their core process of action tokenization often involves a trade-off between reconstruction fidelity and inference efficiency.
We introduce \textbf{FASTer}, a unified framework for efficient and generalizable robot learning that integrates a learnable tokenizer with an autoregressive policy built upon it.
FASTerVQ encodes action chunks as single-channel images, capturing global spatio-temporal dependencies while maintaining a high compression ratio. FASTerVLA builds on this tokenizer with block-wise autoregressive decoding and a lightweight action expert, achieving both faster inference and higher task performance.
Extensive experiments across simulated and real-world benchmarks show that FASTerVQ delivers superior reconstruction quality, high token utilization, and strong cross-task and cross-embodiment generalization, while FASTerVLA further improves overall capability, surpassing previous state-of-the-art VLA models in both inference speed and task performance.

Published as a conference paper at ICLR 2026
FASTER: TOWARD EFFICIENT AUTOREGRESSIVE VISION LANGUAGE ACTION MODELING VIA NEURAL
ACTION TOKENIZATION
Yicheng Liu Shiduo Zhang2. 36+, Zibin Dong Baijun Ye Tinyyan Yuan Xiaopeng Yu Linqi Yin Chenhao Lu Junhao Shi Luca fiang-Tao Yu Liangtao Zheng', Tao Jiang', Jingjing Gong"t Xipeng Qiu Hang Zh
"Tsinghua University, FFudan University, SShanghii Innovation Institute, 'Galaxea 'Tianjin University, 'Hong Kong University, ?UCSD
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Figure 1: FASTer combines a learnable action tokenizer (FASTerVQ) and an autoregressive VLA model (FASTerVLA), achieving efficient compression, fast control, and strong performance across eight real and simulated embodiments.
ABSTRACT
Autoregressive vision-langueen language-actioon (VLA) models have recently demonstrated strong capabilities in robotic manipulation. However, their core process of ac- tion tokenization often involves a trade-off between reconstruction fidelity and inference efficiency. We introduce FASTer, a unified framework for efficient and generalizable robot learning that integrates learnable tokenizer with an autore- gressive policy built upon it. FASTerVQ encodes action chunks as single-channel images, capturing global spatio-temporal dependencies while maintaining high compression ratio. FASTerVLA builds on this tokenizer with block-wise autore- gressive decoding and a lightweight action expert, achieving both faster inference and higher task performance. Extensive experiments across simulated and real- world benchmarks show that FASTerVQ delivers superior reconstruction quality, high token utilization, and strong cross-task and cross embodimen generaliza- tion, while FASTerVLA further improves overall capability, surpassing previous state-of-the-ard VLA models in both inference speed and task performance.
'Equal contribution. 'Corresponding authors. 'Project leaders.
fuy cheng238mat hangzhaogmar) .al. edu.on (sdzhang?38m, xpqive]. eddan.edu.cr, jigongés1 1eronngsst.ed..or .edu.cn

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INTRODUCTION
Vision-LanuuageAAction (VLA) models represent a paradigm shift in robotics, embodying gener- alist robot policies trained on increasingly large-scale robotic datasets (Chenjia Bai, 2024). These models are categorized primarily by their method of robot action prediction, with the most promi- nent approaches being diffusion based (Team et al. 2024 Black et al. 2024) and autoregressive
VLA (Belkhale & Sadigh 2024 Kim et al. 20244 Pertsch et al. 2025 Zhou al. 2025) mod- els. While diffusion- based models have demonstrated superior precision in manipulation tasks, they often exhibit a notable deficiency in leveraging critical visual and linguistic cues (Pertsch et al., 2025 Dong et al. 2025). In contrast, recent research indicates that a carefully designed autoregres- sive VLA model can increasingly bridge the performance gap with its diffusion-based counterparts, while simultaneously offering enhanced cestructiio-followwer
capabilities (Pertsch et al. 2025 In- telligence et al. 2025 Hancock al. 2025). ). superior scene generalization (Pertsch et al. (025)] and
effective transfer of common-senne sense knowledge (Brohan et al., (203)). Most importantly, autoregres-
sive VLA models share the most architectural similarity to the highly successful Vision-Language Models (VLMs), suggesting significant potential for future advancements.
pivotal challenge within autoregressive VLA models is the development of an appropriate tok- enization scheme to discretize continuous robot action sequence into action tokens (Wang et al.) 2025c: Pertsch et al. 2025). Numerous sequence modeling studies, including LLMs and Speech- LLMs, have demonstrated that tokenizer quality directly determines model performance (Radford et al. 2019; Zhang et al., 2023) Gong et al.] (025). A further challenge for autoregressive VLA models is their inference efficiency, they considerably slower than diffusion- counter- parts (Kim et al. 2024] Intelligence et al.) 2025). From this perspective, an effective action tokeniza- tion method must fulfill four key requirements: i) High compression efficiency: It must generate a minimal number of tokens for long sequences to ensure efficient and fast inference. ii) Robust reconstruction quality: As fewer tokens reduce the available information space, is essential to guarantee high reconstruction fidelity rather than pursuing compression at the expense of accuracy. iii) 2D structural modeling: the tokenizer must account for the awo-dimensional nature of action sequences- gruuencs--comprisngg both the action dimension, where each component carries distinct physical semantics with limited redundancy, and the temporal (horizon) dimension, which often exhibits sub- stantial redundancy across time. Modeling these coupled structures jointly is essential to achieve favorable trade-off between efficiency and accuracy. iv) Flexibility: The tokenizer should enable out-of-the-box applicability across different backbones, tasks, and embodiments, thereby serving as a measure of its generalization ability. However, our preliminary experimental results, as illus- trated in Figure [ indicate that existing tokenization methods fail to comprehensively satisfy these principles.
To address these challenges, introduce FASTer (Flexible Action Sequence Tokenization for efficient inference), a unified framework consisting of two complementary components: FASTerVQ and FAS FASTerVQ first non-uniformly groups the action sequence into semantically meaningful patches, effectively mitigating the imbalance of data distribution across different ac- tion dimensions. A hybrid transformer encoder then employed to extract latent representations, which are subsequently quantized via residual vector quantization (RVQ) (Parker et al.) 2025 Lee al.) 2022). This design achieves superior balance between reconstruction fidelity and compres- sion ratio- pitio-preeeeving fine-grained motion details even under a significantly smaller token budget. The tokenizer is trained to reconstruct actions in both temporal and frequency domains, enabling to capture local dynamics and global trends simultaneously. Pretrained on large-scale robot datasets, FASTerVQ exhibits strong generalization across embodiments and tasks, achieving extremely high compression rates while maintaining near-lossless reconstruction, even for digh-diimesiooal control such as whole-body coordination. Building upon FASTerVQ. FASTerVLA employs block-wise autoregressive decoding that leverages the structured latent space and the partial independence of tokens along the action dimension, enabling efficient parallel prediction within each block while maintaining coherent spatio-temporal structure. Furthermore, lightweight action expert, architec- turally aligned with the VLM backbone yet highly parameter-efffcient, is introduced to bridge the modality gap between linguistic reasoning and continuous control. Together, these innovations al- low FASTerVLA to achieve faster and more stable inference, stronger multimodal alignment, and consistent state-of-the-arr performance across diverse tasks, embodiments, and model scales. Our contributions are summarized as follows:
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We propose FASTerVQ. compact and gegh-ccoppeesion-ratee action tokenizer that combines transformer- based residual vector quantization (RVQ) with a lightweight mixture mechanism. This design jointly compresses action sequences into a unified discrete codebook while preserving control-relevant structure, enabling downstream VLAs to more effectively leverage its outputs for action reasoning and generation.
We introduce block-wise autoregressive decoding for efficient action-token modeling, together with share structured action expert that aligns with the VLM backbone. These components jointly unleash the capability of autoregressive VLAs, allowing them to achieve faster inference while maintaining high accuracy.
We establish a comprehensive benchmark covering four real robots and four simulated environ- ments, providing the first systematic analysis of action tokenization for VLAs. Extensive exper- iments demonstrate that FASTerVQ achieves superior trade-offs between reconstruction fidelity and code length, and that FASTerVIA consistently delivers state-of-the performance across embodiments, tasks, and VLM backbones in both simulated and real-world settings.
RELATED WORK
Dision-Languager
models. With advances in LLMs and multimodal systems (Sun et al.,
2024 Achiam et al. 2023: Bai et al. 2023 Dubey et al. 2024), Dision-Language-aee
( (VLA)
models have emerged as a promising direction for universal manipulation, leveraging the scalability
of pretrained VLMS (Brohan et al. 2023: Driess et al. 2023) Kim et al.) 2024 Black et al.) 2024]
Pertsch et al., 2025 Intelligence et al. 2025). Continuous control is typically modeled through either -iffusion-based methods (Bjorck et al. 2025 Black et al. 2024: Kim al. 2025) or autore-
gressive prediction over discretized actions (Brohan et al. 2023] Kim et al.] 2024 Pertsch et al.]
2025). Although autoregressive VLAs offer strong language grounding and generalization, they main slower than non- non-autoregressive approaches (Brohan et al.
2023 Pertsch et al.) 2025| Black
al. 2024). Our method instead learns compact, structured discrete action space that aligns with pretrained VLMs, allowing autoregressive policies to operate at a more efficient token granularity and bringing inference speed into a practically usable regime.
VQ Tokenizer. VQ tokenization is widely used for modal compression across images Yu et al, 2024 Sargent et al. 2025 Tian et al. 2024 Bao et al. 2002), video YYu al.,
2023 Tang al.
2024
Zhao et al. 2024 Wang et al. 2024a), audio (Casanova et al.
2024|
Parker et al. 2025
Zhang et al. 2023; Lahrichi al. (025), graphs (Wang et al.. 2025a
Zeng al.
2025
| guyeen
et al. 2024), and multimodal fusion (Sadok et al. 2025 Liu et al. 2025
Wang et al.
2024b). Audio
codecs and action tokenizers share key traits: both process continuous time-series with short-term fluctuations, long-term trends, and periodic patterns (Parker et al., 2025 eh (l.,5 both face
non-uniform information density (Parker et al. [025 Zhang et al. 2023); and both require strong temporal causality to ensure sequence coherence (Lahrichi et al. (025). FASTerVQ therefore adopts an audio-inspired residual architecture, and our experiments confirm that, with sufficient data, such VQ designs inherit the strong generalization and scaling behavior observed across modalities.
Action tokenization. Autoregressive VLAs require discretizing continuous actions. Early ap- proaches serialize each action dimension (Brohan et al. 2023] Kim et al. 2024), while later meth- ods flatten full action chunks into 1D sequences (Zhao et al.] 2023] | Chi et al. [024] Pertsch et al.)
2025 Belkhale & Sadigh 2024) using relative (Black al. 2024] Intelligence et al. 2025) or
delta actions (Pertsch et al. (2025). Existing cicenizers-binnine (Brohan et al. 2023 Kim et al.
2024 DCT+BPE (Pertsch al.] 2025), and VQ variants (Belkhale Sadigh 2024 Wang et al.
2025ch either under-compredd over- wer-fragment, or lack reconstruction fidelity, producing action vo-
cabularies that remain challenging for AR VLAS to model.
Block-wise generation. Chunk-wise generation appears across modalities: long-video models syn- thesize segments sequentially, conditioning each chunk on the last frame of the previous one (Zhang Lim 2024; Yang et al., 20255). speech models adopt dynamic chunk- prediction for faster au- toregressive synthesis (Li et al.| 2025), and language models explore multi-token decoding or block- wise diffusion to reduce sequential depth (Li et al., 2024b) Arriola et al. 2025). In robot control, chunk policies (Zhao et al. 2023) are induced to mitigate compounding error, while (Zhang et al. 2025) show that chunked causal transformers further reduce autoregressive steps. (Kim et al. 2025 also performs parallel chunk decoding. Our approach instead performs block-wise autoregression over discrete action codes, enabling fast multi-token prediction while preserving AR consistency.
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Figure 2: FASTerVQ. (a) The raw action sequence is patchified into compact tokens based on their embodied configuration. (b) FASTerVQ applies DCT and reconstruction losses and adopts RVQ, encoding actions into Ne code levels; each level can be reshaped into a C₁, Ca tensor. METHOD
Problem Formulation. We consider the problem learning a VLA policy that maps multimodal observations to sequences of robot actions. At each time step, the policy receives three inputs: an RGB image I E R x3, proprioceptive state 8, E IRd,, and a language instruction I L. The (a,, Gt+1. ... Qc-H-1).
output is action sequence of horizon length H. denoted as Au++ Instead of predicting continuous actions directly, we represent each action chunk as a sequence of discrete codes (21,-...) =N) obtained via VQ tokenizer. The policy is then trained in autore- gressive manner to generate these codes conditioned on (I,, st, 1). The VQ tokenizer subsequently decodes the generated codes back into continuous actions.
A key bottleneck of autoregressive VLA models lies in the token sequence length. Since tokens generated sequentially, each requiring full forward pass through large transformer, inference latency grows quadratically due to attention complexity with respect to sequence length. Moreover, correct decoding requires all tokens to be predicted accurately, making the model brittle when token lengths vary. This issue is particularly acute in whole-body control, where high degrees of free- dom lead to long action sequences. For example, the FAST tokenizer requires 150-200 tokens to represent a 2-second motion, which corresponds to an inference delay of roughly three seconds. Our experiments with policies initialized from multiple VLMs further confirm that training a VLA on variable-length codes is substantially more challenging than fixed-length representations. To overcome these limitations, we propose FASTerVQ and EASTerVLA, which compress action rep- resentations, reduce the effective autoregressive depth. and equip the policy with lightweight expert pathway attuned to action-level structure. The next sections unfold these components in detail.
3.1 FASTERVQ
Our action tokenizer architecture comprises two main components: an action patchifier and a transformer- bassed Residual Vector Quantization (RVQ) Tokenizer. This design leverages the unique characteristics of robotic action sequences to enhance tokenization efficiency and fidelity.
Action Patchifier. Although robot action sequences could be fed directly into transformer sequential vector, a preliminary patching step proves to be more effective due to two key properties of these sequences. First, robot actions often exhibit smoothness and temporal redundancy, which can be addressed by chunk-wise grouping. This approach increases both the compression rate and the information density per token. Second, the different dimensions an action vector correspond to distinct physical quantities with highly non-uniform data distributions. For instance, robot's s gripper state often binary (open or closed), and the base movement is typically zero during arm manipulation. This distributional imbalance can pose training challenges. Grouping similar physical quantities a priori effectively mitigates this issue. Specifically, for an action sequence A (a,, a.+1.... --1H-1), where a, E R. perform two-dimensional partitioning. The temporal dimension is uniformly divided into m groups length And the action dimensions are non- uniformly partitioned into groups based on their physical characteristic (e.g., end-effector position, orientation, and gripper state are grouped separately). Each group is then padded to the largest group, d. This process yields a structured tensor of shape (m (m-h) (n dd), which is subsequently flattened into set of patches, af a H- E (lm-)))((-a)) This procedure be conceptualized form of
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non-overlapping convolution, method widely adopted for early data processing in various domains due to its proven efficacy (Yu et al. 2023 Liu et al. 2023b).
Residual VQ Action Tokenizer. Inspired by the success of Residual Vector Quantization (RVQ) and transformer-basee codecs (Parker et al. 2024), de-
Algorithm 1 FASTer Tokenizer
Require: FASTerVQ quantizers (0:) - coder dene and decoder Φlee.. procedure ENCODE((;(+*)
sign a transformer- -based hybrid encoder-decoder ar- chitecture named Transformer Action AutoEncoder (TAAE). It combines the adaptive global receptive field of transformer and the local relation modeling and downsampling capacity of convolutions, forming an effective information bottleneck. Moreover, RVQ naturally imparts coarse-to-fine structure: early stages capture low-frequency components, while later stages refine high-frequency residuals. This property not only improves representational efficiency but also stabilizes both the training and inference of down- stream VLA models. For an input action patch a the encoder thene downsamples it into latent embed- ding 2 E RCxx xC. We then apply RVQ with No quantization levels. decomposing into residuals as ri = z, ri+1 = r; - (r.), and the quantized la- tent embedding is Zy E (r,). Each quan- tizer Q₁ selects its nearest codebook entry eg with index k = arg miny [|r; e, Collecting these indices yields discrete code tensor C [1...., (2....,21)) N. at stage i and location (h, a). This tensor C' serves as the action tokens for the downstream policy. The quantized embedding 2, is then passed through the decoder diae via straight-through estimator (STE) Van Den Oord et al. 2017) to reconstruct the action patch f
en-
Dene PPnt(Pathhi (0ca+H))
C+ [] 2q + for i = to N₂ do
2q += (r). - 2r(r)
C.append(cs)
return C procedure DECODE(C)
By f
for i = 1 to N. do
2q += 2, @o.lookup(C(i)
Gr++ <- UnPatchi Fy (edeo(
return Gt
procedure VQ.TRA
@u:t+ DECODE (ENCODE (a))+
Train with the loss L in Equation
where each element CtAb.a denotes the index of the chosen codebook vector
Training Objective. The action tokenizer is trained by minimizing the reconstruction loss free and the commitment loss Loummit- The reconstruction loss Lre: composed of two components: an & loss on the temporal action signal afu+ for step-wise action reconstruction, and an & loss on the Discrete Cosine Transform (DCT) of the signal, (CT(af++H) to capture the overall trend. We select the & loss due to its robustness against the extreme value noise often present in real-world robot action data, which contributes to training stability:
C Am:4 a.:+ Ih + (CTT( DCT( + A 1||= sg(z)))))) ( 12.
where sg denotes the stop-gradient operation, and À balances the loss components. The RVQ code- books are updated using an exponential moving average (EMA) with dead codes reinitializing.
3.2 FASTERVLA
Architecture. As shown in Figure B (a), FASTerVLA follows most VLM structure-2 vision tower, a projection layer, and a transformer-basee based language backbone-to ensure compatibility with stan- dard pretrained checkpoints. Visual inputs are encoded by the vision tower and projected into the model dimension; language instruction follows the usual text pipeline. Action embeddings & Pro- prioception encoding: We resize the original embedding table to include |C] new slots for action codes. Proprioceptive states are discretized into integers and tokenized as text. Positional encoding and spacing augmentation: We adopt rotary position embeddings (RoPE) for positional encoding. Since actions are encoded at a fixed sequence length, naively predicting fixed-length target can lead to position overfitting. To mitigate this, we apply spacing augmentation: during training, the relative offset between adjacent action tokens is perturbed around unit spacing, i.e., if p. denotes the posi- tion of token i, we set p:- = Pi-1 + + E;. where E; is a small integer jitter uniformly sampled from (e.g., k 2), while preserving token order. At inference, we revert to fixed spacing (e.g.. P: = Pi-1 + 2). This encourages the model to rely on content rather than absolute positions within the horizon. Lightweight action expert: Inspired by ma (Black et al. 2024). we add a lightweight action expert sharing the backbone architecture but with fewer parameters. The backbone encodes the multimodal context once, while the expert autoregressively decodes action tokens from these
U [0, k]
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(6) AR&BAR Causel Attention Mask
Figure 3: FASTerVLA. (a) The model takes RGB images, proprioceptive states, and language instruction as inputs to a transformer-basee discrete action tokens, which VQ decoder maps into the final continuous action sequence. (b) Codes are generated codebook -wise before advancing along the temporal horizon, yielding greater stability than horizon-fiit first decoding (red arrows denote the decoding order). (c) Left: vanilla causal mask; Right: Block-wise causal mask, where tokens within each block attend to preceding and intra- block tokens. Token colors denote different modalities.
VLM. An Action Expert autoregressively generates
features. This design mitigates interference with pretrained weights while enabling lightweight and efficient decoding.
Block-wise Autoregression (BAR). In the vanilla setting, the policy is trained autoregressively with next-token prediction objective under teacher forcing:
(2) where C' = -CI....,.. CN) denotes the sequence of discrete action codes produced by the VQ to- kenizer, and N = N₂ x Cn x C. the total number of tokens per action chunk. At inference, tokens are generated sequentially until the <eos> symbol, and decoded by the VQ decoder into continuous trajectory A
CAR Σ
t=1 log pil cs Ceis st,
key inefficiency of vanilla AR arises because many action codes are only weakly coupled across dimensions: distinct action dimensions often carry independent physical semantics and heteroge- neous distributions. To address this, adopt a block-wise objective that predicts the next block of tokens single forward pass. Specifically, we partition into contiguous blocks of size B, i.e., {C1,... Cj} with C, (c,,1...,, C),B) and N B. Training still uses teacher forcing. and the BAR loss is defined as
LUaR --
Ei_ log poley, à Cej, st,z).
We replace the standard causal mask with block wise causal mask that allows intra- block atten- tion (Fig. 3p)) To integrate text and action generation, BAR uses two control tokens, (BoBIk) and (FoBlk), toggle between block-wise and standard AR prediction, enabling seamless switching within a single sequence. In practice, when the model outputs (BoBIk), this token is replicated B times and fed back as input initiate prediction of the first block.
Decoding order. The VQ tokenizer outputs action codes arranged as a 3D tensor C E &NexCxxCs spanning codebooks, temporal horizons, and action dimensions. FASTerVLA organizes decoding hierarchically across both the codebook and horizon dimensions (Fig. Bp). For each codebook, the model first decodes tokens along the horizon dimension L...., C₁. before proceeding to the next codebook. This ordering aligns with the residual quantization pipeline, where earlier codebooks capture coarse, wow-frequeccy components, and later ones refine higher- frehur-fy residuals. Conse- quently, decoding progresses in a coarse-to-fine manner, improving representational efficiency and stabilizing both training and inference. Since each block is decoded in parallel, BAR reduces the number of forward passes from N to roughly N/B. As shown in Table 6 the number of blocks is small (e.g., on LIBERO), yielding up to a 3x reduction inference latency compared to To (Blckk et al. 2024).
4 EXPERIMENTS
We comprehensively evaluate the proposed FASTer framework through series of experiments cov- ering both the tokenizer and the full VIA model. We first investigate FASTerVQ to understand its

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LIBERO
Simpler-Bridge
Model
Spatial
Object
Goal
Long
Averuge
Spoon
Carrot
Block
Eguplant
Average
Diffusion Policy (Chi et al.
2023
78.3
92.5
68.3
50.5
72.4
Octo-Base Team a.
78.9
85.7
84.6
51.1
75.1
12.5
8.3
0.0
43.1
16.0
SpatialVL. Qu A025
83.2
89.9
78.6
55.5
78.1
16.7
25.0
29.2
100.0
42.7
do (Black et 20754
96.8
98.8
95.8
85.2
94.2
66.7
58.3
58.3
88.3
66.7
Reo (ineeligence
2025
98.8
92.4
OpenVLA-UFF (Jm cl
202.5
97.6
98.4
97.9
94.5
97.1
12.5
4.2
8,3
0.0
6.25
UniVLA (Bu
96.5
96.8
92.0
66.7
50.0
43.8
OpenVLA (Kim al. 2024
84.7
88.4
79.2
53.7
76.5
32.0
30.0
18.0
38.0
29.5
Palligemma
55.8
64,8
64.4
31.2
54.1
66.7
29.2
12.5
54.2
40.9
MiniVLAA BBllkhale & Sadigh 2024
77.0
68.0
44.0
70.0
14.0
49.0
VQ-VLA Want RUASS
75.2
60.0
12.5
8.0
6.3
To FAST-R
Pertsch cimi 20125
96.4
96.8
88.6
60.2
85.5
29.1
21.9
10.8
66.6
32.1
FAST-D Pertach
96.6
97.2
96.0
86.8
94.22
77.5
88.3
68.3
71.7
76.5
FASTer w/o BAR
99.4
98.8
94.8
88.6
95.4
97.5
83.3
65.0
78.3
81.0
FASTer
98.0
99.4
98.6
95.4
97.9
91.7
93.3
67.5
99.2
87.9
Table 1: Policy performance on Libero and Simpler-Bride benchmarks.
% Success Rate
% Task Progress
% Sucoose Rate
% Task Progresa
Task Progress
Teak Progresi
Task Progress
63
Libaro (Scratch)
VLABanch (NN-D)
AVERAGE
[Bimaruui Skm)
(Raasi Word))
(Amanual Resi-Wordd)
(WRG Rend-WWWW)
Find
FASller
Figure 4: Policy performance across embodiments and environments. Results reported in the in- distribution setting, covering real-world embodiments and three simulated setups.
properties action tokenizer and then benchmark FASTerVLA across diverse tasks, embodi- ments, and backbones. Further ablation studies (4.4) analyze key design factors such as the tokenizer
architecture and codebook size. These experiments aim to answer the following questions:
1) How well does FASTerVQ balance accuracy and efficiency in modeling action chunks?
(4.
2) How flexible is FASTer across tasks, embodiments, and backbones? (4.2
3) How strong are the performance and generalization abilities of FASTerVIA? (4.3)
4) How do tokenizer performance and action code distributions affect overall VLA behavior?
4.3
4.1 EXPERIMENT SETUPS
Benchmarks. We evaluate across nine benchmarks spanning five distinct embodiments in both simulated and real-world settings as illustrated in Figure [I] These tasks include deformable manip- ulation, whole-body control, instruction following, and long horizon manipulation. Details of benchmarks and the setup of each task provided in Appendix(A.]
Baselines. We begin by evaluating FASTer on the most widely used public benchmark, comparing it against prior state-of-the-an models spanning both non-autoregressive (top) and autoregressive (mid) architectures in Table As other experiments' baselines, we include To (Black et al.) 2024), a non-autoregressive VLA with flow matching, and -T-FATT (Pertsch et al. 2025). ) the state-of-the thetee-rrtt autoregressive VLA built on the FAST tokenizer.
Training. Unless otherwise specified, all baselines and FASTerVLA models in our experiments initialized from checkpoints pretrained on large-scale robotics data (e.g.. from -ASST) For Bridge and Droid experiments, all VLA models are instead initialized from pretrained VLM weights and pretrained on the same dataset to ensure a fair zero-shot evaluation. Detailed training configurations are provided Appendix A.2
To FAST-D: delta action chunks; To FAST-R: relative action chunks. All results use me FAST-D unless otherwise noted.
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(ASTeeOX))
BrdgatheS) CIVVAn (ps)e-3)
Uberoh-10
Xhrmib-10
VOVLA
FAST
FAS (er(L)
Figure 5: Average VRR of different action tokenizers under multiple error tolerances a. FASTer achieves the best performance across all scales and exhibits clear data-scaling behavior.
Figure 6: Compression rate vs. reconstruction trade-off across action horizons, where FASTer achieves the best balance, particularly for long sequences.
4.2 RESULTS AND ANALYSIS OF FASTERVO
As introduced in Section D an effective action tokenizer should jointly achieve high reconstruction quality and high compression rate. Figure [ and Figure [6] compares FASTer against Fast and other VQ tokenizers in terms of compression efficiency and reconstruction fidelity. During training, we observed that reconstruction loss alone often fails to capture meaningful action quality, as many low-level discrepancies correspond merely motor sensor noise rather than task-relevant devia- tions. In such cases, perfectly reconstructing noisy signals yields little practical benefit, since actions with minor deviations typically result in indistinguishable outcomes during real-world execution. To better capture functional fidelity, we introduce the Valid Reconstruction Rate (VRR), which mea- sures the proportion of reconstructed actions whose deviation from ground truth falls within a small tolerance threshold:
(4) where Notal is the total number of actions, Nealid counts those within tolerance a, qucc and a denote reconstructed and ground-truth actions, 1(-) is the indicator function, and a is a hyperpa- rameter. For robot end-effector translation, corresponds to the Euclidean distance error measured in meters, whereas for end-effector rotation and joint positions. a represents an angular error in radians. In practice, reconstruction error on the order of 10 is sufficient cause noticeable degradation in task execution accuracy.
VRR Naalia /
Numal:
Neald = Σ Moul ([|a, (|aa,mm
- [[aa""=aaaaa]) <)),
FASTerVQ Combines Compactness and Fidelity. In Figure and Figure while baseline VQ methods perform well in terms of compression ratio, they exhibit significant shortcomings in recon- struction accuracy-eve evenn on in-distribution datasets, as shown in Figure [3 As a result, the LLM backbone learns inherently erroneous action codes, which constrain overall performance, especially in preccssreiiionn tasks. In contrast, FASTerVQ achieves a favorable balance between compression and fidelity, preserving reconstruction accuracy while minimizing the number of tokens. Figure further compares tokenizer performance across different control frequencies and sequence lengths. Thanks its structured design, FASTerVQ maintains significantly higher compression efficiency on high-frequeee action sequences.
FASTerVQ Efficiently Leverages Larger Data. A competent action tokenizer is expected to be capable of effectively scaling with large amounts of training data. To evaluate this property, trained three variants of FASTerVQ on different amounts of FASTEFFTTERRR (S), FASTER(L), FASTER (XL), and the details can be found in Table 3 As shown in Figure 5 FASTerVQ consis- tently delivers of-te-of-the-ar reconstruction across multiple error tolerances σ. e.g., FASTerVQ(S) vs. FAST and FA FFA)T)VQ((L) vs. FAST+ This capability clearly exhibits strong data-scaling trend. In particular, FASTerVQ-XC achieves nearly lossless action-chunk reconstruction at the physically meaningful tolerance level of 10 even when FASTerVQ is trained with data budgets that are equal to or smaller than those of FAST. Furthermore, our results confirm that FASTerVQ's scaling behavior yields substantial gains in generalization across tasks, embodiments and even action rep- resentations demonstrated in Figure and Figure 13 We will discuss this in detail in the following section.
VO Generalizes Across Embodiments. We evaluate FASTerVQ's generalization by train- ing the tokenizer solely on single-arm delta-EEF trajectories, and testing it tasks that differ in
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VAR (cm1e-3)
15.00 94,00
+3.251
Figure 7: Comparison of FASTer. its non-BAR variant, and FAST across backbones the Libero benchmark.
Figure 8: FASTer generalizes well across em- bodiments and action types with scaling trend.
semantics, embodiment, and even action representation. For unseen tasks and embodiments, we test on newly collected Widow and XArm datasets (Figure 13). For unseen action representations, we evaluate joint-velocity actions from Droid, absolute joint-position actions from Galaxea Open, and delta joint-position actions from Agilex (Figure ( Across all settings, FASTerVQ maintains strong reconstruction performance, suggesting that action chunks from diverse robot platforms share a common structure once mapped into normalized action space. This indicates that FASTerVQ cap- tures a transferable action prior and shows a clear data-scaling trend in both cross-embodimees and cross-action-Vee generalization.
4.3 RESULTS AND ANALYSIS OF FASTERVLA
In distribution Performance of FASTerVLA. We first evaluate the performance of FASTerVLA and several SOTA baselines in an in-distribution (ID) setting. This setting is characterized by us- ing the same tasks and scenes as in the training set, but with randomized object layouts to ensure robustness. The evaluation is conducted across comprehensive set of task suites, including three simulation environments: LIBERO. VLABench, and GalaxeaManisim, and three real-world robotic platforms: single-arm using xArm, the bimanual using R Ritee, and whole-body control using RlLite (Figure [T]. Our results (Table T Figure 4 show that FASTerVLA achieves 97.9% success rate on the Libero benchmark, setting a new state of the art. Across all evaluated settings, it consistently outperforms prior baselines, with only marginal gap тoo on VLABench. These findings high- light the strong potential of autoregressive VLAs-when equipped with an efficient tokenizer like FASTerVQ to rival and even surpass diffusion-basee approaches.
Performance of FASTerVLA. To evaluate the generalization and robustness of FASTerVLA, we conduct tests in an out-o--distribution (OOD) setting where the training and evaluation tasks are distinct. This evaluation spans both simulation and real-world environments, including the VI.ABench suite and two physical platforms: WidowX (with VLAs pre-trained on BridgeData (Wakke et al. (023)) and Franka (pre-trained on DROID (Khazatsky et al.) (024)). In simulation, FASTerVLA achieves the highest overall success rate while exhibiting the lowest relative performance drop (29%) compared to its in-distribution baseline (Figure 9. On the real- world robotic platforms, it consistently outperforms prior baselines (Figure 10 In particular, on the Simpler-Bridge benchmark, FASTerVLA attains 87.9% success rate, outperforming the second- best model by 12.9%, further demonstrating its superior generalization capability.
To better understand these results, we analyze the code distributions of different tokenizers on BridgeData (Table [8. Fast+ (57% of 2048) and FASTerVQ (100% of 4096) exhibit markedly higher codebook utilization than Fast (48% of 2048). Moreover, Fast shows a dominant token with Fmax = 10%, while Fast+ and FASTerVQ achieve higher normalized entropy, indicating more balanced and information-rich token usage. Importantly, this balanced utilization translates into stronger task performance (Figure 10), where FASTerVLA and Fastt achieve the highest task progress across zero-shot benchmarks such as Bridge and Droid. Overall, these findings suggest that diverse and evenly activated codebooks enhance action representation expressiveness, leading to better generalization and higher success rates.
Cross-backbone ablity of FASTer. FASTer framework can also adapt to different backbones. We evaluated FASTerVLA with Palligemma (Steiner et al., 2024), Qwen2.5 (Yang et al. 2025a Gao al. 2025), and VL3.5 (Wang al. 2025b) the Libero benchmark (Figure 7. FASTer consistently improved performance, most notably raising VL3.52L33.-2B'' success rate by 17.3% to 96.65%, turning it from the weakest with FAST into the strongest overall. This gain stems from FASTer's concise, regularized, and data-driv aata-driven representation, which better matches instruc- tion distributions and avoids the inefficiencies of variable-length tokens in FAST. The figure shows

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Published as a conference paper at ICLR 2026
% Success Rate
% Success Rate
10%
In-distributian
Cross-Catagon
Comman-Sense
Semantic PiOs
Texture Disturbance
Average
PIO
Fast+
FASTer
Figure 9: Evaluation results of generalization ability on VLABench. The evaluation dimensions
cover generalization of language, vision, and goal and knowledge transfer.
Progress
Task Progress
% Task Progress
model part
Time(Single)
Time(WBC)
image encoders
16 ms
23ms
40
observation forward pass
72 ms
105ms
AR forward pass
6.4*21
20
D
BAR forward pass
7,4ms*3
8.51ms*12
FASTer detokenization
2.7 ms
7ms
0-
AVERAGE FASTer
Inference
112ms
23/ms
Pl0
Fast
Fast+
Table 2:
The inference time is measured us- ing a PyTorch implementation on an RTX 5090. WBC refers to whole-body-control setting that uses an action DoF/chunk size of 21/32, whereas Single refers single-arm setting that uses 7/20.
Figure 10: Comparison of zero-shot perfor- mance between FASTer and baselines on Droid and Bridge datasets.
that FASTer's improvement is driven primarily by its neural VQ tokenizer: swapping FAST for FASTerVQ yields most of the gain, with BAR adding only a smaller incremental boost. Additional results reported in Appendix A.4 A.4
Inference Efficiency of FASTer. We evaluate the inference efficiency of FASTer on an RTX 5090, using PyTorch implementation across the LIBERO and RILite-WBB benchmarks. In the Sin- gle setting, the single-arm configuration in the LIBERO environment uses an action chunk size of 20 with two camera views; in the WBC setting, the 21-DoF configuration size of 32 with three camera views. A detailed analysis is provided in Table 2 The results sug- gest that the dominant bottleneck lies in the observation encoding stage: both To and our model spend approximately 58-127ms on this step. By comparison, the action tokenizer remains relatively light, contributing only about 2.7-7ms. We also an AR version of FASTer to compare with BAR. Because BAR emits multiple tokens per forward pass, it can meaningfully reduce latency when a task requires large number of action tokens. Under the same environment and compu- tational setup described above, we further compare FASTerVLA, To. and FAST with shared PaliGemma backbone in Table 5 On LIBERO, the runtimes are 112ms for FASTerVLA, 176ms for тo. and 197-556ms for To FAST. In the RILite-WBC tasks, where the action dimensionality substantially larger, FASTerVLA and Ty converge to similar runtimes (around 230ms), since FASTerVLA performs 12 forward passes. In contrast, To FAST incurs significantly higher cost of 1.100-3,000mm driven by the considerable number action tokens required during decoding.
action chunk
4.4 ADDITIONAL STUDIES
We ablate key design choices of FASTerVQ (tokenizer, codebook size, and residual depth) and FASTerVLA (action expert and block-wise decoding). The results confirm that the final configura- tion achieves the best balance of accuracy, efficiency, and stability. Detailed results and analysis are provided in Appendix A.3
Real-world rollouts of PASTerVLA are shown in Figure
CONCLUSION
We propose efficient autoregressive VLA framework that couples flexible vector quantization module with a efficient VLA training inference setting. delivers strong task performance with low-latency inference and generalizes across backbones, as the pretrained VQ be reused in downstream tasks without retraining. The success stems from three key ideas: a codec-inspired RVQ balancing accuracy, diversity, and code length; block-wise autoregressive decoding for faster, expressive inference; and lightweight mixture-of VLA for action tokens. These results show that autoregressive modeling can be fast, transferable, and scalable for multimodal generation.
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ETHICS STATEMENT
The authors have adhered to the ICLR Code of Ethics. This work does not involve human subjects, sensitive data, or raise any direct ethical concerns. All datasets used are publicly available.
7 REPRODICIBILITY STATEMENT
We are committed to ensuring the reproducibility of our research. Our source code will be made public upon publication. Detailed descriptions of our methods and model architectures are available in the section 3 All experimental settings, including datasets, training hyperparameters, evaluation settings are specified in the section and Appendix.
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Xinyu Zhang, Yuhan Liu, Haonan Chang, Liam Schramm, and Abdeslam Boularias. Autoregressive action sequence learning for robotic manipulation. IEEE Robotics and Automation Letters, 2025.
Tony Z Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn. Learning fine-grained bimanual manipulation with low-cost hardware. arXiv preprint arXiv:2304. 13705, 2023.
Yue Zhao, Yuanjun Xiong, and Philipp Krähenbühl. Image and video tokenization with binary spherical quantization. arXiv preprint arXiv:2406.07544 2024.
Hongyi Zhou, Weiran Liao, Xi Huang, Yucheng Tang. Fabian Otto, Xiaogang Jia, Xinkai Jiang, Simon Hilber, Ge Li, Qian Wang, et al. Beast: Efficient tokenization of b-splines encoded action sequences for imitation learning. arXiv preprint (iXix:250000677.2 2025.
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A APPENDIX
A.1
DETAILED EVALUATION SETUPS.
Singlo Arm Tasks
Bimanual Manipulation Tasks
Pretraining Evalustion
Generalization Abillyy Evaluation
Simulation
Liber
GaloseatkampSim
Smpler-Hooee
VLABenci
Adaptability
Manipulation
Real-World
Xarm Stite
Droid 3 of
Bridge
RTLite Suite
: Pretraining effectiveness; N: Fitting and adaptation abillt; Generalizaion abilly
Figure 1I: Experiment setups. Diverse embodiments, scenes, and tasks assessing multiple capability dimensions (marked by emojis).
Benchmarks&Evaluaaa Details. We evaluate FASTerVLA across multiple dimensions, includ- ing out-of-the-box performance after pre-training, adaptability through fine-tuning on downstream tasks, and generalization ability under visual and language perturbations as well as in zero-shot settings. We adopt variety of benchmark suites with distinct focuses, encompassing diverse robot embodiments and task distributions. For a fair ablation, we make every effort to use the same training settings the other baselines. The evaluation setup below lists our training recipes and evaluation protocols.
Libero (Liu et al. 2024) includes four task suites Spatial, Object, Goal, and Long-with with a total of 40 tasks, each containing 50 examples. This benchmark is particularly suitable for assessing both the models' capacity to fit the data and their adaptability to downstream tasks. To further compare the advantages of different model architectures, we evaluate two settings: models pre- trained and then fine-tuned on Libero, and models trained directly on Libero via imitation learning from scratch. We train single policy across all four task suites. The results are reported in Table [ and Figure 4 respectively. Following the official fine-tuning recipe, we train the model under a multi-tast joint training setting for 30k steps, using global batch size of 32, with image augmentation disabled, 8 H100 GPUs. This model leverages FasterVQ(S), as listed in Table
Simpler-widow (Li al. 2024a) serves benchmark for assessing the pre-training perfor- mance of VLAss. Table reports the evaluation results of models trained from scratch on Bridge dataset (Walke al. 2023). The models were trained for 4 epochs with image augmentation abled, using a global batch size of 128 across 8 H100 GPUs. FASTerVLA utilizes Faster as Unlike
described Table Unlike the default Simpler settings, which evaluate 24 trials for each task, evaluate each task for 120 trials to mitigate the noise introduced by the high variance in zero-shot evaluation.
VLABench (Zhang et al. 2024 benchmark focusing on the generalization capabilities of VLAs. We finetune models on 10 tasks from VLABench, with total of 5,000 examples. VLABench offers rigorously controlled evaluations across multiple dimensions, including com- plex instruction following, visual disturbances, language grounding, and commonsense transfer. We fine-tune the pretrained models-aTo. To FAST, and FasterVLA-on 5,000 diverse episodes covering 10 tasks from VLABench. All models were trained for 30k steps with global batch size of 32, no image augmentation, and executed on 8 H100 GPUs. The tokenizer used in FASTer was trained separately on the VLABench primitive dataset.
GalaxeaManipSim (Team) 2025). Using this simulator, we evaluate the model on seven dual- arm tasks- Baaks-BBoocHammerBBee. BlocksStackEasy, ContainerPlace, DiverseBottlesPick, DualBot- tlesPickHard, EmptyCupPlace,, and ShoePlace with total of 7,000 examples. The embodiments used in the simulator are R1 and RILite. We chose To, -ASTT as the baselines. All models were trained for epochs with image augmentation enabled, using a global batch size of 128 across H20 GPUs. We use three camera views, including the head camera and the left and right wrist
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Table 3: Overview of data mixtures used for training FASTerVQ.
Mixiure Name
Datasets
Ratio
Application
Galaxea
Galaxeu Open Dataset
Diag et al
2025
1.0
Used for RlLite Evalunion.
Droid Mixture
Droid (Joint Velocity)
Khazatsky et al. 2024
Used for Draid evaluation.
FASTaiS)
Libera
eL al
20241
5.0
Used for libero, simpler and widow evaluntion.
Bridge
Walke
2045
Libero
Li ct
2024
5.0
Bridge
Walke of
21125
TASTenL)
Kuka (K
Khhikdy et ml:
211
1.0
Used for Xurm finetuning and tokenizer scaling unalysis.
Brohan
Droid(EEF)
Kiaazalsky
2024
1.0
Liberu eLu 2024)
5.0
Bridag Walke
Kuka
Sainkoo el F1 Pu16
1.0
FASTe(()))
Fracfal [Siihhnn
Used for tokenizer scaling and action representation analysis.
(Io) Khazalsky dl M024
1.0
Droidiooit Velocny] (Klazalsky
2024
Galaxea Open Dataso
Drang 0f 2025
1.0
cameras. The FASTerVQ was trained on the Galaxea Open Dataset (Jiang et al. 2025) without post-training.
Xarm Suite features an XArm dataset that covers five tasks, with 500 real-world episodes. These tasks involve flexible object manipulation, multi-goal selection, and long-horizon planning. The tasks include: board wiping, poker choosing, fruit placing, object managing, and towel folding. We conducted joint training on 500 samples across these tasks, using total batch size of 128 with image augmentation, for 30k training steps. We employ FasterVQ(L) in this model, as shown in Table
RiLite Suite includes three roal-world tasks to evaluate system: two desktop-level tasks- picking cubes and table bussing- and one whole-body control task, making the bed. The dataset sizes 200 demonstrations for picking cubes, 200 demonstrations for desk organization, and 1,000 demonstrations for bed making. We chose To. FF-T as the baselines. We train our model and baselines for 8 epochs, using a global batch size of 256 across 64 H20 GPUs. The FASTerVQ was trained on the Galaxea Open Dataset (Jiang et al. 2025) without post-training.
Bridge (Walke al. 2023) is large-scale manipulation dataset with over 60k trajectories. Fol- lowing its hardware setup, we use Widow250s for zero-shot evaluation after pre-training. The checkpoints used in this experiment are the same those in Simpler.
Droid (Khazatsky al., 2024) contains over 90k real-worke robotics trajectories. To ensure con- sistent evaluation, we follow its hard setup and evaluate models in a zero-shot setting after pre-training. Following (Intelligence et al., 2025), we train on the Droid dataset for epochs using 64 H200 GPUs, with PPr batch size of 16, resulting in a total batch size of 1024, and adopt joint-velocity control.
A.2 IMPLEMENTATION DETAILS
Data Mixture. In the experiments of Section 4 we trained FASTerVQ with different data mixtures. We report the detailed mixtures in Table
FASTerVO. We trained all policies using AdamW with a learning rate of 1 10-4, weight decay 0.1, and ($1. By) (0.9, 0.95). The learning rate followed a cosine decay schedule with 1,000 warmup steps. Gradients were clipped norm 1.0, and training used mixed precision (bfloat16). The single-arm policy (8M parameters) was trained with a batch size of 512 sequences of length 21 tokens, whereas the full-body policy (13M parameters) was trained with a batch size of 2048 sequences of the same length. All models were trained on 8x H100 GPUs, and training converged within 300k steps. We set the codebook size to K 4096, the latent dimension to 128, and the commitment cost to 3 0.25. The detailed hyperparameters and settings used for each evaluation environment are provided in Table 4
FASTerVLA. We trained the VLA using AdamW with learning rate of 2.5 x 10 and weight decay 1 x 10 10, The learning rate followed a cosine decay schedule with 1,000 warmup steps, and training used mixed precision (bfloat16). The attention dropout was set to 0.0. For inference,
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Table Detailed Hyperparameter Configuration for FASTerVQ and BAR.
Suite
Embodiment
Action Duf
BAR block size
Action chunk size
C.
C₁
Number of codebook
Lābero
Frunka 7
?
Simpler
Widow250s
6
7
1
VLABBenhh
Franka
7
GalascaManipSim
R1. RJLite
14
14
2
Xarm Suite
XArm
Rte Suice
Riliite
21
32
21
2
Bridge
W5dow250
Droid
Franka
7
20
1
Table 5: Inference time across environments and models, measured on an NVIDIA GeForce RTX 5090 using Py PyTorch.
Model
FASTer
pi0
FAST
Environment
LIBERO (ms)
112
176
197-556
R1Lite-WBCC (ms)
237
225
1,100-3,000
employed top-k sampling (A 50) with temperature 0.8. Block-wise autoregressive decoding was used, with a block size of for WBC and bimanual tasks, and for the single-arm setting. The hyperparameters used for action tokenization and BAR listed in Table We set the conditioning window to step, and the final actions were clipped to the range [-1,1].
A.3 ABLATION STUDY
VASTerVQ Ablations. As shown in T FASTERVQ exhibits clear performance trends across the three ablated dimensions. The TAAE tokenizer delivers the strongest reconstruction fidelity, reflected by the lowest & loss, and also reaches the highest success rate, surpassing both the CNN and Transformer variants. Increasing the codebook size improves representation capacity to 4096 entries, which point success rate and utilization peak before degrading at 8192, indicating the onset of codebook collapse. Finally, residual quantization yields the most stable gains with two to three residual layers, whereas deeper stacks introduce diminishing returns or mild instability. Together, these observations support the final choice adopted in our design: a TAAE tokenizer coupled with 4096-entry codebook and three residual codebooks, which jointly provide the most favorable balance between reconstruction quality and policy success rate.
FASTerVLA Ablations. We examine two essential components of VLA: the action expert (AE) responsible for action tokenization, and the block-wise autoregressive (BAR) decoding mechanism. Table reveals that on libero, AE consistently enhances SR relative to the no-AE baseline, with pretraining providing the most pronounced gain (97.9 95.5). On simpler-widow, AE trained from scratch collapses (23.6), suggesting that gradients from the VLM offer only a faint signal for learning high-quality action tokenizer. Pretraining therefore becomes indispensable, lifting SR to 87.9. These observations naturally motivate a two-stage training scheme: first pretrain AE within the VLM, then fine-tune jointly with partial freezing. This schedule accelerates convergence and markedly stabilizes optimization. For decoding, BAR substantially improves both accuracy and efficiency: SR rises from 95.5 96.7, 7, while latency is reduced by more than 2x (323,m ms I m40.ms). When BAR is combined with AE, SR further increases to 97.7 at the same low latency, offering the most harmonious balance between precision and speed.
We also ablate different BAR block sizes. Our intuition is that BAR becomes effective when the action-token sequence exhibits a relatively stable length and when meaningful correlations are pre- served across tokens. Under such conditions, BAR can be applied directly and remains reliable. We evaluate multiple BAR block sizes using the same FASTerVQ configuration with an action code length of 42. As shown in Appendix A.3] performance remains stable as long as the block size lies within a reasonable multiple of the action- dinon--imenioon code length. When the block size matches this code length, the result is the strongest. Empirically, these findings echo design intuition: BAR integrates seamlessly with FASTerVO because FASTerVQ preserves the latent structure of action chunks, allowing BAR exploit this structure for coherent block-wise generation.
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Table 6: Ablation on FASTERVQ policy success rate in libero simulator (SR, %) and & loss (lower is better);
Tokenizer
(b) Codebook
(c) Residual
f1
utilization
CNN
96.2
0.0027
512
95.4
100
93.4
Transf.
0.0036
1024
93.2
100
TAAE
97.9
0.0021
4096
97.9
99.6
3
97.9
95.1
Table 7: Ablation studies of FASTer. Left: ablation study of the action expert (AE) on libero; Middle: AE on simpler-widow; Right: BAR decoding. "Pretrain" indicates whether the model is initialized from a checkpoint pretrained on robotics datasets; others without this label are all initialized from such pretrained checkpoints.
libero (AE)
simpler-widore (AE)
bar decoding
Variant
SR
Variant
SR
Decuding stralegy
SR
Lalency (ms)).
No
AE
75.6
Token-wise
AE (no pretrain)
94.8
AE (no preiruin)
23.6
Black wise
96.7
140
(with pretrain)
(with pretrain)
Block-wise (FASTer)
97.7
140
A.4 DETAILED RESULTS OF DIFFERENT VLM BACKBONES
To evaluate the effectiveness and adaptability of our framework, we conducted comprehensive ex- periments the Libero benchmark. We selected several popular VLM backbones: Paligemma-3B (Steiner et al. 2024). InternVL3.5-2B (Wang et al. 2025b), and various sizes of Qwen2.5 (0.5B, 1.5B, 3B) Yang et al. 2025a). For Paligemma and VL, utilized their original VLM check- points. For the Qwen2.5 models, we adopted the VLM checkpoints provided by (Gao et al. 2025). which fine-tumed the base LLM with vision tower and projector on the LLa Va v1.5 dataset (Liu al. 2023a). All these backbones were subsequently trained from scratch on the Libero benchmark. We compared their performance against the baseline FAST tokenizer.
The detailed results in Table 9 demonstrate that our framework achieves consistent and significant performance improvements across all tested model architectures and parameter scales. Notably, while Paligemma-3B delivered the strongest performance among all models using the FAST tok- enizer (93.50 Average), our framework instance, Intern VL3.5-2B, which had the weakest performance with the FAST tokenizer (79.35 Av- erage). was elevated become the top- perfforrrning model overall (96.65 Average) when paired with our tokenizer. This is particularly significant given that Intern VI. 3.5 is inherently competitive on embodied benchmarks, indicating that our method successfully unleashes latent potential for ac- tion generation. This remarkable turnaround not only proves the effectiveness of our framework but also highlights its broad adaptability and its ability to fully harness the capabilities of diverse VLM backbones.
able unlock the latent potential of other VLMs. For
Table 9: Detailed performance of different VLM backbones Libero
Model
Tokenizer
Spatial
Object
Goal
Long
Average
FAST
92.60
95.80
70.00
78.20
84.15
Qwen2.5-0.5B
Ours
90.40
99.00
71.60
87.00
87.00 (1 2.85)
FAST
96.00
92.80
87.20
85.80
90.45
Qwen2.5-1.5B
Ours
94.40
97.80
92.00
84.80
92.25 (1 1.80)
FAST
97.80
93.80
90.20
83.40
91.30
Qwen2.5-3B
Ours
96.40
99.40
92.00
94.00
95.45 (1 4.15)
FAST
92.60
95.20
77.20
52.40
79.35
Intern VL3.552B
Ours
97.80
98.60
97.20
93.00
96.65 (1 17.30)
FAST 93.40
98.80
95.00
86.80
93.50
Paligemma2-33
Ours
96.60
98.00
95.20
89.40
94.80 (1 1.30)
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Peramanne inritiann
Metric
Fast
Fast+
FASTer
Nvocab 1
2048
2048
4096
Usage [56]
48.39
57.37
100.0
Nactive 10- 4
16
Fmax [%]]
9.63
1.82
1.35
Entropysorm
0.69
0.77
0.91
0-3
Table 8: Comparison of vocabulary distribu-
tions of different tokenizers on the Bridge.
Figure 12: Different BAR block sizes, including irregular granularity, and their performance on the LIBERO environment.
A.S
QUALITATIVE ANALYSIS OF THE TOKENIZER
To evaluate the effectiveness of different action tokenizers, we conduct qualitative and quantitative analysis covering reconstruction fidelity, token utilization, and generalization across datasets and embodiments.
VQ Settings. The following VQ tokenizer configurations are used in our reconstruction visualiza- tions and token usage analyses, covering prior baselines and our proposed model.
MiniVLA's VQ (Beikhale Sadigh, 2024) uses residual vector quantization to encode action chunks into sequences of codeword indices, with optional extra tokens in the vocabulary. For our experiments, we use the official VQ weights released by the authors: one trained on Libero and the other on Bridge V2 Dataset.
VQ-VLA's VQ (Wang et al., 2025c) replaces Open VLA's simple binning with residual VQ kenizer that encodes action sequences into discrete codeword indices using hierarchical quantiza- tion. In our experiments, we pretrain the VQ from scratch using the VQ-VLA training framework and code, on 1:1 mixture of LIBERO and BRIDGE with single H200 GPU (batch size 1024, 800k steps, learning rate 5e-5).
Fast+ (Pertsch et al.] 2025)is the universal action tokenizer of Fast, trained on one million real- world robot trajectories.
VQ is our proposed VQ, with the training data mixtures summarized in Table 3
Action Reconstruction Visualization. We qualitatively assess the learned VQ models by visual- izing normalized action sequence reconstructions from Libero, Bridge, XArm, and Widow (Fig- ure 14), comparing MiniVLA, VQ-VLA, Fast+, and Faster in terms of fidelity and generalization. In each plot, the red line denotes the ground-truth trajectory, the green line denotes the reconstruc- tion, and the shaded red region denotes the error band with tolerance of 0.01. Note that the green horizontal line in the figure appears due to clipping in the normalization at the 99th percentile bound.
On in-domain datasets such Libero and Bridge, most predicted chunks remain within the 0.01 eтror band, indicating reasonable fidelity. However, their performance degrades notably on out- of-domain datasets like XArm and Widow, where larger fraction of predictions exceed the toler- ance. Reconstructions from MiniVLA and VQ-VLA exhibit more frequent spiky artifacts, Fast+ and VQ preserve stable reconstructions across both seen and unseen domains, with most chunks remaining within the error band and some trajectories exhibiting nearly exact overlap with the ground truth.
Token Activation Statistics. Table TO compares codebook size, active tokens, and usage frequency across VQ tokenizers. Small codebooks (VQ-VLA, MiniVLA) are fully activated. Fast+ under- utilizes its larger vocabulary. In contrast, FasterVQ effectively harnesses a much larger codebook, activating thousands of tokens with a meaningful subset above frequency thresholds. This avoids token collapse and provides richer, more fine-grained representations.
Detailed VRR Evaluation Results. Figure 5 in Section 4 presents the average VRR values across four datasets for the FASTer, Fast, MiniVLA-VO. and VQ-VLA-VO tokenizers under different scales. Figure I3 provides a more detailed view, showing the VRR scores for each dataset at the three 0 thresholds corresponding to meaningful physical error levels. Across multiple 0 scales, the
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VRR (d-1e-2)
VRR (d-Se-3)
VRR (0-1e-3)
VoIMinwaal
0.3789
0.4387
G4144
0.2286
VOJMirlds)
0.1407
0.2343
0.3209
0.2110
VOIMiikial
0.025
2.1207
0.2158
0.0422
0.3888
0.7723
0.3733
0.1166
0.6
84ST
1.0000
1,0000
1,0000
1.0000
Rasse
0.906m
0.9909
0.9950
0.9900
Raste
0.0677
0.0116
0.0017
PASTer1S
1 0000
1,0000
0.0999
0.0954
(85))o(())
0.87701
0.9011
0.9920
0.9220
RASTivSS
0 5200
0.5870
0.6935
0.6135
0.
IASTenL1
10000
1.0000
0:5999
0.9956
FASTerL))
0.9991
0.9998
0.9999
0.9976
MSSTenL
09033
3,907
0.9867
0.9029
607
Jam
Wodow
801
Brige
1arm
Wilzo
Libero
Plaoe
Jam
Hoton
Figure 13: Heatmap for detailed VRR of different tokenizers across ID and OOD datasets.
Table 10: Token activation statistics on Bridge and XArm-nixture. Columns show codebook size, active tokens, and counts of tokens exceeding the 10-3 and x 10 thresholds.
Bridge
XArm
Tokenizer
CB
Act.
le-3
> 26
CB
Act.
le-3
2e-2
VQ-VLA-vg (800k)
256
256
256
256
MiniVLA-vq-bridge
256
256
256
256
Fast+
2048
1175
200
2048
1067
216
VQ
4096
4096
162
4096
4096
38
results show that previous VQ-based methods exhibit extremely poor reconstruction performance, which implies that the VLA backbone is effectively learning biased supervision signal. Under the same amount of training data, FASTer(S) significantly outperforms Fast. Moreover, even when Fast+ is trained with substantially more data than FASTer(L), FASTer(L) still surpasses Fast+ in reconstruction quality.
A.6 USE OF LLM
We utilized LLMs writing assistance tool during the preparation of this manuscript. The use of LLMs was strictly limited to polishing the text, which included improving grammar, refining sen- tence structure, and enhancing overall clarity and readability. The research concepts, method- ologies, and conclusions were developed entirely by the authors.
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MinivlaVVQ
Vqvla-VQ
Fast*
Faster
LIBERO
Widow
Figure 14: Visualization of action reconstruction results from MiniVLA, VQ-VLA, Fast+, and FasterVQ representative trajectories from the Libero, Bridge, XArm, and Widow datasets.
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WICW
DOC
Figure 15: Visualization of our evaluation tasks.
23


Reviews:
[{"review_id": "AG2g2OHFOa", "reviewer": "Reviewer_8e91", "summary": "The paper proposes a new learnable tokenizer scheme based on residual vector quantization (RVQ) for robotic manipulation datasets. A custom action patchifier is used to group timesteps and physically similar action dimensions together which is subsequently fed into an RVQ model learning a coarse-to-fine representation of action signals. The tokenizer is trained on a combination of RVQ losses alongside local and global action reconstruction objectives. Furthermore, a custom VLA architecture utilizing the new tokenizer is presented which adds block-wise autoregressive prediction of multiple tokens with a custom decoding order to speed up inference.", "questions": "-\tIt seems that BAR decoding in combination with FASTer leads to significantly higher success rates than just using FASTer. Could we expect similar gains when using BAR in combination with other tokenizers? I would be curious to see the performance when using the FAST tokenizer with BAR to fully determine the benefits of FASTer versus FAST.\n-\tSimilar to the previous question, I  would be curious how much using BAR decoding offsets the potentially slow tokenizer.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "-\tOverall, the paper is well written and clearly motivates the drawbacks of previous tokenizer designs in VLA models. It encompasses the most relevant previous work and gives a clear outlook of which open problems in the field it tackles.\n-\tThe proposed tokenizer (FASTer) for VLA models addresses relevant challenges of action tokenization, such as achieving good reconstruction quality with high compression rates. The choice of RVQ for this task seems adequate and timely: Learnable tokenizers have shown strong initial results in robotics but rely on partially manual design for converting continuous signals into discrete codes.\n-\tThe use of a custom decoding order in combination with the RVQ tokenizer is well thought out, resembling the strengths of diffusion-based action prediction heads (going from low frequency to high frequency reconstruction) without the need for diffusion training. \n-\tAn extensive list of experiments is conducted, comparing FASTer VLA with relevant baselines such as pi0 using the FAST tokenizer on a wide range of benchmarks. Aside from inference speedups, FASTer VLA achieves broad success rate improvements across the board with only VLABench lacking behind, suggesting strong generality of the method.\n-\tThe performance of the FASTer tokenizer itself is also analyzed properly, showing good scalability and generalization to out of distribution data.", "weaknesses": "-\tAside from the LIBERO experiments in Table 1, most results seem to combine FASTer VLAs blockwise autoregressive decoding (BAR) with the FASTer tokenizer, making it difficult to judge the impact of the tokenizer itself on success rates. Optimally, a tokenizer should be mostly agnostic to the choice of architecture. This is partially shown in Figure 6 with different VLM backbones but still might rely on custom decoding schemes (see first question).\n-\tThe encoding times of the FASTer tokenizer seem high (150ms on LIBERO) compared to the runtime of the base pi0 model. To the best of my understanding, the paper does not isolate the tokenizer runtime as a standalone metric in the inference speed results.", "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": "RABJpi49SS", "reviewer": "Reviewer_8Nbo", "summary": "This paper is about making (auto-regressive) vision language action models more useful in contexts like robotics where fast inference and precise reconstruction are important for overall system performance and inference time impacts which action frequency tasks can have. The main focus is on the action tokenization part. The approach designs action tokens (action representation) by performing residual vector quantization and  trains reconstructing action sequences in the time and frequency domain (loss functions). This is supposed to result in compact and highly compressed action sequences. The inference approach infers multiple tokens in parallel by block-wise decoding. The paper also establishes a larger benchmark, collecting several real robots and simulations.\n\nImportantly, this approach is supposed to surpass non-auto-regressive models in inference speed and reconstruction accuracy.", "questions": "- When introducing the VRR measure for evaluation, the authors write that a parameters is set \"corresponding to an allowed error of approximately 1cm\". It is not clear what this means. Is this 1 cm Euclidean error at the end-effector pose (in case of manipulation)? For many robotic tasks, 1 cm error is substantial and means that the execution of the task will fail. The authors might want to elaborate on the VRR. What happens if the threshold is set of 0.5 cm or 1 mm?\n\n- The related work section makes little effort to contrast the proposed method with the state of the art. While the experiments speak for themselves in terms of quantitative results, it would be an improvement if the authors could also argue the improvement over the state of the art conceptually.", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is nicely written and pedagogical, especially the introduction section and the related work section.\n\n- The general idea of patchifying the continuous actions seems very reasonable.\n\n- RVQ is generally very suitable for this problem.\n\n- Adding the DCT to the step-wise reconstruction loss makes sense for capturing the overall trend as long as the action sequences behave with trends within horizon H.  \n\n- The paper contains a large set of experiments and evaluations which provide insight in the performance and tradeoffs of the proposed method.", "weaknesses": "- The reusability in some figures is low due to the choice of colors, lack in contrast, and lack of clear captions. Page 7 is a good example for this issue. Red-green contrasts should be avoided in general. \n\n- There are a few types in the manuscript, repeated words, and wrong references (e.g. to Fig. 5 on page 7).", "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": "9d89Zi8MqG", "reviewer": "Reviewer_DL7K", "summary": "This paper studies the action tokenization problem, which is valuable for performance and efficiency of autoregression-based policy. The authors propose FASTer, an improved version upon FAST, by incorporating learnable vector quantization (instead of fixed procedure) to improve the compactness and fidelity of action tokenization.", "questions": "- How does it work with diverse robots and action types? If I train a single FASTer tokenizer for multiple robots, does this mean (1) a slower encoding/decoding process, (2) a less compact coding? \n\n- Fig7 shows FASTer underperforms than Pi0 in in-distribution tasks. I understand the emphasis is OOD performance in that figure, but why is that? \n\n- Does the space augmentation only change position embedding, or actually change the sequence values? If the latter, is there an example of the space augmentation? \n\n- Regarding the block-wise autoregression, I am very interested in the step size (block size) in the autoregression. In the paper, the step size = B (size of one codeblock). But what would the performance possibly be if the block size is 1 or N, or other number? Does a irregular granularity will affect autoregression performance?\n\n- Is it possible to provide some failure cases and related analysis?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "- The paper studies an important problem that carries strong practical applications. \n- The proposed method makes sense, is simple yet effective. \n- The proposed method is extensively evaluated with different metrics, in diverse robot environments, including real-world.", "weaknesses": "- The texts in many figures are quite small and not easy to read especially when printed, particularly in Fig. 2 and Fig. 3.\n\n- Page 4 details the action tokenization process and components. I believe it would be easier for readers if there is an algorithm block that outlines this procedure, like FAST does. Currently, it takes some effort to gather everything from text. \n\n- [1] is a recent work that incorporates autoregressive action chunk generation without an explicit action tokenizer, and its proposed technique is very relevant to the block-wise autoregression. The authors are recommended to include this in the related work.   [1] Autoregressive Action Sequence Learning for Robotic Manipulation (RAL 2025)", "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": "NNd31yp0qo", "reviewer": "Reviewer_uL4Q", "summary": "The paper introduces FASTer, an approach for discrete-token action generation for VLA policies:\n\n- **FASTerVQ (tokenizer)**: A transformer autoencoder with residual VQ (RVQ) over an action patchifier. Training uses a dual-domain reconstruction loss (temporal L1 + DCT-domain L1) to preserve both low and high-frequency motion. The result is a compact, balanced code usage with improved reconstruction.\n\n- **FASTerVLA (policy)**: An autoregressive (AR) policy that (i) adds an action-expert head, (ii) uses RoPE spacing augmentation to avoid positional overfit on fixed horizons, and (iii) employs Block-wise Autoregressive (BAR) decoding with a coarse-to-fine order under a block-causal mask. The paper additionally sketches a “real-time chunking (RTC)” regime for asynchronous control via stochastic training and lightweight logit ensembling.\n\nAcross simulators and real-robot setups, the method reports higher success rates and lower inference latency than prior AR tokenizers (e.g., FAST/FAST+), with competitive results to continuous flow/diffusion policies in some regimes. Ablations examine tokenizer design (codebook sizes, RVQ depth, losses) and decoding choices, and provide analysis on vocabulary utilization and reconstruction-error vs compression.", "questions": "- How does Block AR differ from similar approaches used in discrete diffusion literature (see Block Diffusion [1])? It would be good to position this paper appropriately and add references.\n- It would be good to have comparisons against $\\\\pi$-0.5 which is an improved version of $\\\\pi$-0 with better generalization.\n- Was a single policy trained for all LIBERO task suite or each LIBERO suite had a separate policy? OpenVLA-OFT attains higher success rate when using a single policy setting, so the paper should clarify here and update the results accordingly. OpenVLA-OFT also has a FiLM variant which improves language grounding. Please take a look at Appendix in [2] for details and update accordingly.\n-  There seems to be some discrepancy in reported latencies for different models and OpenVLA-OFT paper Table III, some clarification here would be helpful. also the main paper should include inference time analysis, detailing out inference latency, action chunk size used and max control frequency supported.\n- Could you provide some insight on Figure 5 (c)? Why compression ratio is so different for different dataset?\n\n\n[1] Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models\n\n[2] Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- While built from known parts (transformer RVQ with time+DCT loss, block-wise causal masks for AR, coarse-to-fine action decoding), the specific combination is novel for VLA setting and practically impactful for AR control policies.\n- Extensive tokenizer analysis (codebook utilization, reconstruction vs compression rates) and sensible ablations on RVQ/codebook design choices. Reported latency reductions are compelling for real-time control discrete AR policies.\n- Method components are described clearly. Design choices are well-motivated (e.g., position overfitting on fixed horizon actions, the coarse-to-fine decode order is intuitive and naturally follows RVQ) accompanied by real world and simulation benchmarks supporting the method.\n- Improving throughput/latency without giving up success is valuable in robotics. The work should be useful to groups deploying AR VLAs where continuous policies are costly to train.", "weaknesses": "It is known that diffusion/flow based are slower to train compared to autoregressive models, the paper should report compute comparison between baselines to accurately place the gains in context. I still believe given sufficient compute continuous diffusion/flow policies would outperform discrete AR policies. AR action heads trained with cross-entropy are brittle for real world control, exhibiting unpredictable bin prediction switches with little observation noise. CE loss is non-metric, it only cares about the probability on the ground-truth bin, no matter how the remaining probability mass is distributed among nearby vs far bins. Continuous policies avoid this by design as they directly penalize errors proportional to deviation. How much does the FASTerVQ tokenization mitigate this?", "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)