VLA-Retain: Benchmarking Cross-Dimension Effects of Vision Tuning in VLAs

Borui Xu; Han Chen; Bingsheng He; Ming Wu; Jason Zeng; Michael Heinrich

cs.CVOct 20, 2026

VLA-Retain: Benchmarking Cross-Dimension Effects of Vision Tuning in VLAs

Authors: Borui Xu, Han Chen, Bingsheng He, Ming Wu, Jason Zeng, Michael Heinrich

Organizations: National University of Singapore

Abstract

Vision-language-action (VLA) models transfer knowledge from large-scale pretraining into robot control, but their manipulation performance remains sensitive to changes in observation conditions, which recent work addresses by fine-tuning the visual module on target-domain data. However, it remains unclear how such adaptation affects robustness beyond the target condition, as existing robustness benchmarks only evaluate models without fine-tuning and adaptation studies only report the target gain. Measuring this cross-dimension effect requires separating target gains from off-target changes, isolating adaptation effects from architectural differences, ruling out generic forgetting, and comparing tuning choices. Therefore, we introduce VLA-Retain, a benchmark that compares five open VLA models, tuned on Camera-view or Sensor-noise data, with their own base checkpoints on LIBERO-Plus dataset spanning seven perturbation dimensions. Our study reveals four findings. First, targeted vision tuning is not local, and its target gain does not predict retention. Mean off-target gain is negative in 8 of 10 model--domain configurations. Under Camera-view tuning, X-VLA gains $69.4$ percentage points on the target but loses $4.6$ points on average elsewhere, whereas OpenVLA-OFT gains $39.3$ and $1.4$ points, respectively. Second, these regressions are not explained only by performance loss on the same tasks without perturbations. Third, broader training coverage raises overall gain over target-specific training but does not eliminate dimension-level regressions. Fourth, tuning strategies are model-dependent, as DELTA-style feature preservation raises X-VLA's mean off-target gain to $+1.2$ points but lowers OpenVLA-OFT's to $-23.1$ . Overall, these findings show that target-domain success alone is insufficient and highlight cross-dimensional retention as a key criterion for targeted VLA adaptation.

Figures & tables

Figure 1: Overview of VLA-Retain. Target-domain data update only the visual module, but the updated module processes observations under every deployment condition. VLA-Retain therefore evaluates each base–tuned pair on the same episodes across seven perturbation dimensions and reports both target improvement and off-target retention.

Model	Visual backbone	Action formulation	Visual share
OpenVLA-OFT ( Kim et al., 2025a )	SigLIP + DINOv2	Regression head	$10.64\%$
$\pi_{0.5}$ ( Black et al., 2025 )	SigLIP	Flow matching	$11.40\%$
InternVLA-A1.5 ( Ma et al., 2026 )	Qwen3.5 ViT	Flow matching	$12.34\%$
NORA-1.5 ( Hung et al., 2025 )	Qwen2.5-VL ViT	Flow matching + FAST	$16.77\%$
X-VLA ( Zheng et al., 2026 )	DaViT	Soft-prompted flow matching	$41.26\%$

Table 1: Architectural coverage of the core benchmark. Visual share denotes the fraction of total parameters contained in the visual backbone and connector.

Dimension	Spatial	Object	Goal	LIBERO-10	Total
Camera view	376	396	408	419	1,599
Robot state	350	398	409	393	1,550
Language	390	354	410	383	1,537
Lighting	292	297	279	274	1,142
Background	258	248	281	289	1,076
Sensor noise	351	422	379	449	1,601

Table 2: Evaluation composition across perturbation dimensions and LIBERO task suites.

Camera-view tuning
	Target success			Off-target change		Overall success
Model	Base	Tuned	$\bm{G^{\mathrm{target}}}$	$\bm{G^{\mathrm{off}}}$	$\bm{G^{\mathrm{worst}}}$	Base	Tuned	$\bm{G^{\mathrm{all}}}$
OpenVLA-OFT	$55.2$	$94.5$	$+39.3$	$+1.4$	$-2.8$	$68.3$	$75.8$	$+7.5$
$\pi_{0.5}$	$70.7$	$92.7$	$+22.0$	$-0.3$	$-3.9$	$84.0$	$87.0$	$+3.0$
InternVLA-A1.5	$80.8$	$83.4$	$+2.6$	$-5.2$	$-12.4$	$83.7$	$79.5$	$-4.2$
NORA-1.5	$6.0$	$34.7$	$+28.7$	$-10.1$	$-17.8$	$47.4$	$43.8$	$-3.7$

Table 3: Target, off-target, and overall results under targeted vision tuning. Success rates are reported as percentages and signed gains are reported in percentage points.

Figure 2: Cross-dimensional success rate changes after targeted vision tuning. Each cell reports a percentage-point change. Blue denotes improvement, red denotes regression, and the outlined column marks the target perturbation.

Figure 3: Statistical uncertainty in off-target retention. Points show mean off-target gains, and horizontal lines show pointwise

95\%

confidence intervals from a paired cluster bootstrap with 20,000 resamples over the 40 source tasks. Red intervals lie below zero, blue intervals lie above zero, and gray intervals overlap zero.

Figure 4: Source-adjusted off-target changes. Results for (a) Camera-view and (b) Sensor-noise tuning. Each cell subtracts the corresponding change on original LIBERO; negative values indicate additional perturbation-specific regression.

Figure 5: Effect of training-data coverage. (a) Overall gains under Camera-view, Sensor-noise, and Mixed training. (b) Dimension-level and overall changes after Mixed training.

Figure 6: Target–retention trade-offs of tuning strategies. (a–b) Tuning scope. (c–d) WiSE-FT.

Model	Strategy	$\bm{G^{\mathrm{target}}}$	$\bm{G^{\mathrm{off}}}$	$\bm{G^{\mathrm{all}}}$
X-VLA	Dual LoRA	$+69.4$	$-4.6$	$+7.0$
X-VLA	DELTA-style	$+62.7$	$+1.2$	$+10.9$
OpenVLA-OFT	Dual LoRA	$+39.3$	$+1.4$	$+7.5$
OpenVLA-OFT	DELTA-style	$+13.6$	$-23.1$	$-16.6$

Table 4: Camera-view results for Dual LoRA and DELTA-style feature preservation.

Appendix figures & tables19 assets

Supplementary material from the paper’s appendix.

Appendix

Category	Configuration	Operation
Reference	Dual LoRA	Rank-32 LoRA on visual backbone and connector
Tuning scope	Backbone-only LoRA	Rank-32 LoRA on visual backbone; connector frozen
Tuning scope	Connector-only LoRA	Rank-32 LoRA on connector; visual backbone frozen
Retention	WiSE-FT	Base–Dual-LoRA visual-weight interpolation
Retention	DELTA-style	Dual LoRA with post-connector feature preservation

Appendix

Table 5: Visual tuning configurations used in the strategy comparison.

Figure 7: Architectures and visual interfaces of the five VLA models. The reference configuration updates the visual backbone and connector and freezes the multimodal backbone and action module.

Model	Vision params.	Total params.	Vision share
OpenVLA-OFT	802.3M	7.54B	10.64%
$\pi_{0.5}$	412.4M	3.62B	11.40%
InternVLA-A1.5	331.4M	2.69B	12.34%
NORA-1.5	668.7M	3.99B	16.77%
X-VLA	362.8M	0.88B	41.26%

Appendix

Table 6: Model parameter scales. Vision parameters include the visual backbone and connector.

	Base	Camera-view tuned		Sensor-noise tuned
Model	Success (%)	Success (%)	Change (pp)	Success (%)	Change (pp)
OpenVLA-OFT	$97.20$	$96.65$	$-0.55$	$96.60$	$-0.60$
$\pi_{0.5}$	$97.35$	$96.65$	$-0.70$	$96.05$	$-1.30$
InternVLA-A1.5	$98.25$	$97.95$	$-0.30$	$97.30$	$-0.95$
NORA-1.5	$94.70$	$81.30$	$-13.40$	$92.75$	$-1.95$
X-VLA	$97.50$	$93.70$	$-3.80$	$96.45$	$-1.05$

Appendix

Table 8: Original LIBERO success before and after targeted vision tuning. Success rates are percentages and changes are percentage points.

Model	Frozen teacher	Matched representation
X-VLA	Base visual path and image projector	image_proj_norm output
OpenVLA-OFT	Base vision backbone and projector	Projector output

Appendix

Table 9: Model-specific feature taps for DELTA-style preservation.

OpenVLA-OFT
Perturbation	Spatial	Object	Goal	LIBERO-10	All suites
Camera view	$48.7/98.9$	$67.9/99.7$	$59.3/93.6$	$45.1/86.4$	$55.2/94.5$
Robot state	$23.1/24.6$	$19.8/19.1$	$21.3/21.8$	$36.6/36.9$	$25.2/25.5$
Language	$95.9/96.4$	$99.7/98.6$	$58.0/59.0$	$95.3/95.0$	$86.5/86.6$
Lighting	$97.9/98.6$	$99.3/100.0$	$97.1/93.9$	$85.0/90.9$	$95.0/96.0$
Background	$88.0/88.4$	$87.5/81.0$	$84.0/88.6$	$83.0/91.0$	$85.5/87.5$

Appendix

Table 10: Camera-view tuning results by task suite. Each cell is Base/Tuned (%).

OpenVLA-OFT
Perturbation	Spatial	Object	Goal	LIBERO-10	All suites
Camera view	$48.7/53.2$	$67.9/73.2$	$59.3/55.6$	$45.1/39.6$	$55.2/55.2$
Robot state	$23.1/20.0$	$19.8/21.6$	$21.3/21.0$	$36.6/37.7$	$25.2/25.2$
Language	$95.9/93.8$	$99.7/99.4$	$58.0/59.3$	$95.3/95.6$	$86.5/86.3$
Lighting	$97.9/96.9$	$99.3/100.0$	$97.1/89.2$	$85.0/90.9$	$95.0/94.4$
Background	$88.0/89.1$	$87.5/90.3$	$84.0/84.3$	$83.0/85.5$	$85.5/87.2$

Appendix

Table 11: Sensor-noise tuning results by task suite. Each cell is Base/Tuned (%).

X-VLA
Strategy	Cam.	Robot	Lang.	Light	Back.	Noise	Layout	Overall
Connector only	$+36.5$	$-4.1$	$-13.3$	$-6.0$	$-5.2$	$-9.6$	$-4.0$	$-0.2$
Backbone only	$+69.4$	$-0.1$	$-4.9$	$-1.8$	$-2.2$	$-10.6$	$-3.7$	$+7.6$
Dual LoRA	$+69.4$	$+0.5$	$-6.6$	$-2.2$	$-3.9$	$-10.7$	$-4.9$	$+7.0$
WiSE-FT $\alpha=0.25$	$+11.6$	$-0.1$	$+0.3$	$+1.7$	$+0.9$	$+0.7$	$+2.0$	$+2.6$
WiSE-FT $\alpha=0.50$	$+32.0$	$+1.4$	$+1.5$	$+0.8$	$+2.0$	$-0.6$	$+1.7$	$+6.0$

Appendix

Table 12: Dimension-level gains under Camera-view tuning and retention strategies. All values are percentage points.

Figure 8: Camera-view checkpoint dynamics. Target, mean off-target, and overall gains are measured relative to the base model on a fixed balanced subset. All gains are reported in percentage points. The target and off-target changes do not evolve monotonically together.

Model	Seed	$\bm{G^{\mathrm{target}}}$	$\bm{G^{\mathrm{off}}}$	$\bm{G^{\mathrm{all}}}$
X-VLA	42	$+70.0$	$-4.0$	$+6.6$
X-VLA	43	$+73.8$	$-5.0$	$+6.3$
X-VLA	44	$+70.0$	$-7.1$	$+3.9$
OpenVLA-OFT	42	$+38.8$	$+1.9$	$+7.1$
OpenVLA-OFT	43	$+40.0$	$+3.8$	$+8.9$
OpenVLA-OFT	44	$+41.2$	$+1.2$	$+7.0$

Appendix

Table 13: Camera-view sensitivity to training seed on the fixed 560-episode subset. All values are percentage-point gains over the corresponding base model on the same episodes.

Model	$\bm{G^{\mathrm{target}}}$	$\bm{G^{\mathrm{off}}}$	$\bm{G^{\mathrm{worst}}}$	$\bm{G^{\mathrm{all}}}$
OpenVLA-OFT	$+39.3$ $[+30.6,+47.4]$	$+1.4$ $[+0.2,+2.8]$	$-2.8$ $[-5.1,-0.9]$	$+7.5$ $[+5.4,+9.6]$
$\pi_{0.5}$	$+22.0$ $[+15.2,+28.8]$	$-0.3$ $[-1.7,+1.2]$	$-3.9$ $[-6.3,-1.6]$	$+3.0$ $[+1.4,+4.7]$
InternVLA-A1.5	$+2.6$ $[-4.0,+9.2]$	$-5.2$ $[-7.3,-3.5]$	$-12.4$ $[-16.1,-8.9]$	$-4.2$ $[-6.5,-2.1]$
NORA-1.5	$+28.7$ $[+22.3,+35.4]$	$-10.1$ $[-13.9,-6.7]$	$-17.8$ $[-26.6,-11.3]$	$-3.7$ $[-7.2,-0.4]$
X-VLA	$+69.4$ $[+61.3,+76.4]$	$-4.6$ $[-7.4,-2.1]$	$-10.7$ $[-15.1,-7.1]$	$+7.0$ $[+4.3,+9.6]$

Appendix

Table 14: Camera-view tuning: summary gains and pointwise

95\%

cluster-bootstrap confidence intervals. Values are percentage points.

Model	$\bm{G^{\mathrm{target}}}$	$\bm{G^{\mathrm{off}}}$	$\bm{G^{\mathrm{worst}}}$	$\bm{G^{\mathrm{all}}}$
OpenVLA-OFT	$+19.8$ $[+13.4,+26.7]$	$+0.3$ $[-1.1,+1.7]$	$-0.6$ $[-5.0,+0.3]$	$+3.4$ $[+2.0,+4.9]$
$\pi_{0.5}$	$+10.2$ $[+6.7,+14.0]$	$-1.3$ $[-2.1,-0.7]$	$-4.1$ $[-6.2,-3.0]$	$+0.3$ $[-0.4,+1.1]$
InternVLA-A1.5	$-1.9$ $[-6.4,+2.3]$	$-2.5$ $[-3.4,-1.6]$	$-6.0$ $[-8.9,-3.7]$	$-2.6$ $[-3.7,-1.5]$
NORA-1.5	$+45.3$ $[+38.6,+51.6]$	$-1.0$ $[-3.4,+1.4]$	$-4.4$ $[-9.4,-2.6]$	$+6.2$ $[+3.7,+8.6]$
X-VLA	$+32.8$ $[+27.5,+38.0]$	$-2.2$ $[-3.8,-0.7]$	$-4.2$ $[-8.1,-2.6]$	$+3.2$ $[+1.6,+4.9]$

Appendix

Table 15: Sensor-noise tuning: summary gains and pointwise

95\%

cluster-bootstrap confidence intervals. Values are percentage points.

Evaluation dimension	Camera-view tuning	Sensor-noise tuning
Camera view	$+39.3\;[+30.6,\,+47.4]$	$+0.0\;[-4.3,\,+4.4]$
Robot state	$+0.3\;[-1.4,\,+2.1]$	$-0.1\;[-1.5,\,+1.3]$
Language	$+0.1\;[-1.4,\,+1.7]$	$-0.2\;[-1.6,\,+1.2]$
Lighting	$+1.0\;[-2.0,\,+3.9]$	$-0.6\;[-4.5,\,+2.6]$
Background	$+2.0\;[-2.5,\,+6.4]$	$+1.7\;[-1.0,\,+4.4]$
Sensor noise	$+7.9\;[+4.7,\,+11.4]$	$+19.8\;[+13.4,\,+26.7]$

Appendix

Table 16: OpenVLA-OFT: paired dimension-level changes and pointwise

95\%

cluster-bootstrap confidence intervals. Values are percentage points.

Evaluation dimension	Camera-view tuning	Sensor-noise tuning
Camera view	$+22.0\;[+15.2,\,+28.8]$	$-4.1\;[-6.0,\,-2.1]$
Robot state	$-0.5\;[-3.1,\,+2.3]$	$-3.5\;[-5.9,\,-1.2]$
Language	$-1.1\;[-3.5,\,+1.5]$	$-1.1\;[-2.7,\,+0.4]$
Lighting	$+3.0\;[+0.4,\,+6.0]$	$-0.1\;[-2.1,\,+1.8]$
Background	$+1.6\;[-1.0,\,+4.4]$	$+0.9\;[-0.3,\,+2.3]$
Sensor noise	$-3.9\;[-6.3,\,-1.4]$	$+10.2\;[+6.7,\,+14.0]$

Appendix

Table 17:

\pi_{0.5}

: paired dimension-level changes and pointwise

95\%

cluster-bootstrap confidence intervals. Values are percentage points.

Evaluation dimension	Camera-view tuning	Sensor-noise tuning
Camera view	$+2.6\;[-4.0,\,+9.2]$	$-6.0\;[-8.9,\,-3.2]$
Robot state	$-12.4\;[-16.1,\,-8.9]$	$-3.4\;[-5.3,\,-1.5]$
Language	$-5.8\;[-9.1,\,-2.8]$	$-2.8\;[-4.9,\,-0.8]$
Lighting	$-1.4\;[-3.8,\,+0.9]$	$+0.7\;[-2.5,\,+5.2]$
Background	$-3.4\;[-7.5,\,-0.3]$	$-3.0\;[-5.9,\,-0.6]$
Sensor noise	$-6.1\;[-11.6,\,-1.8]$	$-1.9\;[-6.4,\,+2.3]$

Appendix

Table 18: InternVLA-A1.5: paired dimension-level changes and pointwise

95\%

cluster-bootstrap confidence intervals. Values are percentage points.

Evaluation dimension	Camera-view tuning	Sensor-noise tuning
Camera view	$+28.7\;[+22.3,\,+35.4]$	$+0.9\;[-0.4,\,+2.2]$
Robot state	$-6.6\;[-11.4,\,-2.0]$	$-4.4\;[-6.9,\,-1.9]$
Language	$-11.9\;[-18.6,\,-5.4]$	$-3.4\;[-6.6,\,-0.3]$
Lighting	$-7.5\;[-18.2,\,+3.3]$	$+5.3\;[-2.3,\,+13.4]$
Background	$-17.8\;[-26.6,\,-9.6]$	$-2.9\;[-9.4,\,+3.3]$
Sensor noise	$-5.9\;[-7.2,\,-4.6]$	$+45.3\;[+38.6,\,+51.6]$

Appendix

Table 19: NORA-1.5: paired dimension-level changes and pointwise

95\%

cluster-bootstrap confidence intervals. Values are percentage points.

Evaluation dimension	Camera-view tuning	Sensor-noise tuning
Camera view	$+69.4\;[+61.3,\,+76.4]$	$-2.8\;[-5.1,\,-0.7]$
Robot state	$+0.5\;[-3.2,\,+4.1]$	$-3.3\;[-6.1,\,-0.5]$
Language	$-6.6\;[-10.1,\,-3.2]$	$-4.2\;[-7.9,\,-0.8]$
Lighting	$-2.2\;[-8.2,\,+3.7]$	$+1.2\;[-1.7,\,+5.9]$
Background	$-3.9\;[-9.0,\,+1.0]$	$-2.2\;[-6.5,\,+1.7]$
Sensor noise	$-10.7\;[-15.1,\,-6.7]$	$+32.8\;[+27.5,\,+38.0]$

Appendix

Table 20: X-VLA: paired dimension-level changes and pointwise

95\%

cluster-bootstrap confidence intervals. Values are percentage points.

Figure 9: Paired X-VLA trajectories under a Sensor-noise perturbation. Six ordered frames are shown for the base failure (top) and Sensor-noise-tuned success (bottom) on the same episode. Frame labels give simulator steps.

Figure 10: Paired X-VLA trajectories under a Camera-view perturbation. Six ordered frames are shown for the base failure (top) and Camera-view-tuned success (bottom) on the same episode. Frame labels give simulator steps.

VLA Model Adaptation

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Sep 16, 2026

Not All Layers Need Tuning: Diagnosing and Directing Adaptation in Vision-Language-Action Models

Fine-tuning a Vision-Language-Action (VLA) model for a new deployment environment is expensive, yet most methods apply uniform-capacity adapters to every network region as if every region requires equal adjustment. This paper tests that assumption on five architecturally diverse VLAs (OpenVLA-OFT,

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Sep 27, 2026

Does Adversarial Training Improve Generalization in Multi-View VLAs? Revealing and Mitigating View Collapse

Vision-language-action (VLA) models adapt pretrained vision-language models (VLMs) for closed-loop robot control, transferring their perceptual and semantic capabilities to action prediction. Despite strong in-distribution performance, however, VLAs often degrade under deployment shifts. Adversarial training (AT) offers a model-adaptive approach to robustness without explicitly anticipating individual shifts, but its effect on natural distribution-shift generalization in multi-view VLAs remains unclear. We study this question using a multi-view VLA directly adapted from a pretrained VLM and evaluate generalization across seven LIBERO-Plus shift axes. Direct AT substantially improves Camera Viewpoint and Sensor Noise, the two shifts affecting only the third-person view, yet produces mixed or negative effects on other shifts. Controlled view interventions reveal a surprising failure mode that we term view collapse: Direct AT can shift cross-view reliance so strongly that the policy becomes dominated by the wrist view. This exposes a robustness shortcut: apparent robustness to a shifted view can arise from reduced use of that view rather than more robust perception of it. This motivates a distinction between robust perception, extracting reliable information under within-view shifts, and robust fusion, adapting reliance across views according to their reliability. To reduce fixed view reliance, we use a simple View Swap intervention and then re-evaluate AT. With View Swap, AT further improves Camera Viewpoint, Sensor Noise, and Robot Initial State, while its effects remain mixed on other shifts. Our results show that multi-view robustness requires separating improved perception from changes in cross-view reliance, and that AT provides selective rather than generic distribution-shift benefits.

Futa Waseda, Shuhei Kurita, Isao Echizen

National Institute of Informatics Tokyo, Japan

Adversarial Training Multi-View Learning Robustness of VLA Models

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May 11, 2026

PriorVLA: Prior-Preserving Adaptation for Vision-Language-Action Models

Large-scale pretraining has made Vision-Language-Action (VLA) models promising foundations for generalist robot manipulation, yet adapting them to downstream tasks remains necessary. However, the common practice of full fine-tuning treats pretraining as initialization and can shift broad priors toward narrow training-distribution patterns. We propose PriorVLA, a novel framework that preserves pretrained priors and learns to leverage them for effective adaptation. PriorVLA keeps a frozen Prior Expert as a read-only prior source and trains an Adaptation Expert for downstream specialization. Expert Queries capture scene priors from the pretrained VLM and motor priors from the Prior Expert, integrating both into the Adaptation Expert to guide adaptation. Together, PriorVLA updates only 25% of the parameters updated by full fine-tuning. Across RoboTwin 2.0, LIBERO, and real-world tasks, PriorVLA achieves stronger overall performance than full fine-tuning and state-of-the-art VLA baselines, with the largest gains under out-of-distribution (OOD) and few-shot settings. PriorVLA improves over pi0.5 by 11 points on RoboTwin 2.0-Hard and achieves 99.1% average success on LIBERO. Across eight real-world tasks and two embodiments, PriorVLA reaches 81% in-distribution (ID) and 57% OOD success with standard data. With only 10 demonstrations per task, PriorVLA reaches 48% ID and 32% OOD success, surpassing pi0.5 by 24 and 22 points, respectively.

Xinyu Guo, Bin Xie, Wei Chai +4

Institute of Automation, Chinese Academy of Sciences · Dexmal · Nanjing University of Aeronautics and Astronautics +1

VLA Model Adaptation Robot Policy Adaptation Robot Skill Learning OOD Generalization Vision-Language-Action Models

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