A1: A Fully Transparent Open-Source, Adaptive and Efficient Truncated Vision-Language-Action Model

Kaidong Zhang; Jian Zhang; Rongtao Xu; Yu Sun; Shuoshuo Xue; Youpeng Wen; Xiaoyu Guo; Minghao Guo

cs.ROApr 7, 2026

A1: A Fully Transparent Open-Source, Adaptive and Efficient Truncated Vision-Language-Action Model

Authors: Kaidong Zhang, Jian Zhang, Rongtao Xu, Yu Sun, Shuoshuo Xue, Youpeng Wen, Xiaoyu Guo, Minghao Guo, +15 more

Organizations: SYSU · Spatialtemporal AI · MBZUAI

Abstract

Vision-Language-Action (VLA) models have emerged as a powerful paradigm for open-world robot manipulation, but their practical deployment is often constrained by cost: billion-scale VLM backbones and iterative diffusion/flow-based action heads incur high latency and compute, making real-time control expensive on commodity hardware. We present A1, a fully open-source and transparent VLA framework designed for low-cost, high-throughput inference without sacrificing manipulation success; Our approach leverages pretrained VLMs that provide implicit affordance priors for action generation. We release the full training stack (training code, data/data-processing pipeline, intermediate checkpoints, and evaluation scripts) to enable end-to-end reproducibility. Beyond optimizing the VLM alone, A1 targets the full inference pipeline by introducing a budget-aware adaptive inference scheme that jointly accelerates the backbone and the action head. Specifically, we monitor action consistency across intermediate VLM layers to trigger early termination, and propose Inter-Layer Truncated Flow Matching that warm-starts denoising across layers, enabling accurate actions with substantially fewer effective denoising iterations. Across simulation benchmarks (LIBERO, VLABench) and real robots (Franka, AgiBot), A1 achieves state-of-the-art success rates while significantly reducing inference cost (e.g., up to 72% lower per-episode latency for flow-matching inference and up to 76.6% backbone computation reduction with minor performance degradation). On RoboChallenge, A1 achieves an average success rate of 29.00%, outperforming baselines including pi0(28.33%), X-VLA (21.33%), and RDT-1B (15.00%).

Figures & tables

Figure 1: Overview of $A_{1}$ . $A_{1}$ comprises a Vision Language Model (VLM) backbone and an action head, where the VLM provides semantically rich and affordance-aware representations for downstream action prediction. We instantiate the latter in two forms: a flow-matching head and an MLP head. To reduce end-to-end inference latency, we introduce a budget-aware acceleration scheme that is compatible with both action-head designs and jointly reduces backbone computation and action-head iterations. Extensive experiments in simulators, on real hardware, and on RoboChallenge demonstrate that $A_{1}$ achieves strong manipulation performance with substantially improved efficiency.

Figure 2: Training and adaptive inference of

A_{1}

. During training, for each layer

i

of VLM, the shared action head generates actions

A_{t}^{(i)}

. For flow matching, the model is executed with the same number of layers

i

to generate predictions. All actions are supervised simultaneously. At inference time, we adaptively activate an appropriate size of VLM based on an exit criterion

c

. The threshold

\eta_{i}

is generated by training set and

c

. When the VLM reaches the

i

-th layer, we computes the discrepancy between the current actions and those from the previous layer, and determines whether to terminate via

\eta_{i}

. We propose Inter-Layer Truncated Flow Matching to accelerate early-exit inference.

Model	LIBERO					VLABench
	Spatial	Object	Goal	Long	Avg.	Toy	Fruit	Painting	Mahjong	Avg.
Octo ( 34 )	78.9	85.7	84.6	51.1	75.1	0	0	6	0	1.5
OpenVLA ( 18 )	84.7	88.4	79.2	53.7	76.5	4	6	40	8	14.5
OpenVLA-OFT ( 17 )	97.6	98.4	97.9	$\mathbf{94.5}$	$\mathbf{97.1}$	-	-	-	-	-
CoT-VLA ( 59 )	87.5	91.6	87.6	69.0	81.1	-	-	-	-	-
MolmoAct ( 20 )	87.0	95.4	87.6	77.2	86.6	-	-	-	-	-

Table 1: Experimental Results of Simulation Benchmarks (%).

Figure 3: Demonstrations show the execution process of $A_{1}$ (second row) and baselines $\pi_{0.5}$ (first row).

Model

UR5

Franka

AgiBot

OpenArm

Dobot-Arm

Mean

stack

arrange

put cup

arrange

fruits

move

pick

clean

tidy

select

cook

pour

blocks

fruits

on coaster

fruits

(small)

objects

glue

table

yellow

vegetable

water

\pi_{0}

( 2 )

\mathbf{100}

\mathbf{70}

\mathbf{80}

\mathbf{40}

40.8

\pi_{0.5}

( 14 )

\mathbf{100}

\mathbf{80}

\mathbf{20}

47.5

A_{1}

\mathbf{100}

\mathbf{40}

\mathbf{50}

\mathbf{80}

\mathbf{80}

\mathbf{20}

\mathbf{80}

\mathbf{20}

\mathbf{56.7}

Table 2: Experimental Results of Real-World Evaluation (%) across Multiple Robot Platforms.

	CLIP	LLM ( $L$ =28)	FM ( $\delta$ =10)	$A_{1}$ -FM ( $\delta$ =10)	$A_{1}$ -FM e ( $\delta$ =10)	$A_{1}$ -FM e ( $\delta$ =2)
Time (s)	0.167	0.612	0.366	1.151	4.443	0.728
GFLOPS	2013.36	9061.01	4.93	11130.30	11503.30	11160.20

Table 3: Computation cost and latency of different parts of the model.

A_{1}

-FM e denotes adaptive early-exit reaching the final layer, where we set

K

=14 to evaluate the exit criterion (Eq. (6)) every two layers.

Figure 4: Example of adaptive inference visualization for the $A_{1}$ model with the exit criterion $c=0.6$ . A successful execution episode from the LIBERO-Long task (instruction: turn on the stove and put the moka pot on it ). Green numbers indicate the layer indices where the $A_{1}$ model exits (the model has 28 layers in total). At each frame, the model outputs actions for the next 8 time steps (action chunk = 8).

Model	Arrange	Hang	Set	Shred	Sort	Stack	Move	Press	Arrange	Arrange
	Fruits	Cup	Plates	Paper	Books	Blocks	Objects	Buttons	Flowers	Cups
DM0 ( 46 )	100	80	100	30	20	100	100	90	70	30
Spirit-v1.5 ( 35 )	80	80	80	20	0	80	80	90	50	0
GigaBrain ( 33 )	60	40	90	0	0	100	90	40	40	80
$\pi_{0.5}$ ( 14 )	40	50	80	0	0	100	50	0	50	0
wall-oss ( 51 )	80	60	50	0	0	100	60	100	20	0

Table 4: Comparison with state-of-the-art open-source VLA models on Table30 benchmark in RoboChallenge. List in success rate. Bold for highest. § Denotes fully open-source models with complete training stack, weights, and data pipelines.

Config	Spatial	Object	Goal	Long	Avg.	TFLOPs	Inf. time
no exit †	98.3	99.3	97.0	88.3	95.8	243.0	17.5
no exit ‡	97.4	100.0	97.4	91.0	96.5	243.0	17.5
c=1.0	97.4	99.8	97.6	91.4	96.6	205.0 (15.6%↓)	20.6 ↑
c=0.7	98.4	99.8	97.4	89.6	96.3	148.1 (39.1%↓)	16.5 ↓
c=0.4	95.6	98.4	95.0	87.0	94.0	100.8 (58.5%↓)	6.8 ↓
c=0.1	96.2	98.2	94.4	80.4	92.3	57.0 (76.6%↓)	5.6 ↓

Table 5: Adaptive early-exit inference with

A_{1}

-MLP. Average accuracy, per-episode computation cost (TFLOPs) and model inference time (second) under different exit criteria

c

on the LIBERO benchmark. † Full-layer training. ‡ Multi-exit training.

$c$ , $\delta$	Spatial	Object	Goal	Long	Avg.	TFLOPs	Inf. time
no exit † , 10	97.2	99.2	94.6	79.2	92.6	231.3	37.9
no exit ‡ , 10	97.4	99.2	96.2	91.2	96.0	229.8	37.8
no exit ‡ , 2	97.4	98.6	96.8	89.6	95.6	226.9	32.2
1.0, 10	97.2	99.6	97.0	91.8	96.4	150.6 ↓	40.9 (7.9%↑)
1.0, 2	94.6	99.0	98.0	90.0	95.4	167.9 ↓	27.5 (27.4%↓)
1.0, 2 ∗	95.4	99.0	97.8	93.2	96.4	156.8 ↓	10.5 (72.3%↓)

Table 6: Adaptive early-exit inference with

A_{1}

-FM. Accuracy, per-episode computation cost (TFLOPs) and model inference time (second) under different exit criteria

c

and denoising steps

\delta

on the LIBERO benchmark. ∗ Layer

i+1

denoising initialized with layer

i

output (Eq. ( 10 )). † Full-layer training. ‡ Multi-exit training.

Method	Spatial	Object	Goal	Long	Avg.	TFLOPs	Inf. time (s)
OpenVLA	19.4	14.0	15.1	14.3	15.6	-	-
OpenVLA-OFT	84.0	66.5	63.0	66.4	69.6	-	-
$\pi_{0}$	60.7	61.4	44.9	48.4	53.6	-	-
$\pi_{0}$ -FAST	74.4	72.7	57.5	43.4	61.6	-	-
$A_{1}$ -FM	86.6	80.0	66.8	58.0	75.3	297.1	36.1

Table 7: Zero-shot results on LIBERO-Plus benchmark.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 5: The $A_{1}$ is deployed on our self-developed dual-arm platform OpenArm.

Configuration	Value
Optimizer	AdamW
Batch size	1024
Total training steps	200K
Learning Rates
ViT backbone	$0$ (frozen)
VLM components	$5\times 10^{-6}$

Appendix

Table 8: Hyperparameters for pretraining

A_{1}

Task / Benchmark	Batch Size	Training Steps	State Mask Prob
LIBERO	128	50K	$0.0$
VLABench	64	50K	$0.0$
RoboChallenge (Aloha)	64	100K	$0.3$
RoboChallenge (ARX5)	32	50K	$0.3$
RoboChallenge (UR5)	64	50K	$0.3$
RoboChallenge (Franka)	64	50K	$0.3$

Appendix

Table 9: Fine-tuning hyperparameters for A1 on downstream tasks. All tasks use AdamW optimizer, ViT frozen (LR=

0

), VLM LR=

5\times 10^{-6}

, Action Head LR=

5\times 10^{-5}

, and visual augmentation (Erasing, Sharpening). The state mask probability is set to

0.5

for all fine-tuning tasks unless otherwise specified.

Config	Metric	Pick glue
no exit †	accuracy	80
c=1.0	accuracy compute↓	70 ↓49.3
c=0.8	accuracy compute↓	70 ↓64.7
c=0.4	accuracy compute↓	80 ↓84.6

Appendix

Table 10: Real-world experiments of adaptive early-exit inference based on AgiBot. Average accuracy and computation reduction ratio (compute↓) under different exit criterion c for model

A_{1}

-FM with

\delta

=2. † Full-layer training.

Figure 6: AgiBot real-world example execution process of $A_{1}$ -FM for early-exit inference with the exit criterion $c=0.4$ . Green numbers indicate the layer indices where the $A_{1}$ model exits (the model has 28 layers in total). Task: Pick up the glue and put it into the pen holder.

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