Fast-dVLA: Accelerating Discrete Diffusion VLA to Real-Time Performance
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866913009010475008 |
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| author | Song, Wenxuan Chen, Jiayi Chen, Shuai Wang, Jingbo Ding, Pengxiang Zhao, Han Qin, Yikai Zheng, Xinhu Wang, Donglin Wang, Yan Li, Haoang |
| author_facet | Song, Wenxuan Chen, Jiayi Chen, Shuai Wang, Jingbo Ding, Pengxiang Zhao, Han Qin, Yikai Zheng, Xinhu Wang, Donglin Wang, Yan Li, Haoang |
| contents | This paper proposes a novel approach to address the challenge that pretrained VLA models often fail to effectively improve performance and reduce adaptation costs during standard supervised finetuning (SFT). Some advanced finetuning methods with auxiliary training objectives can improve performance and reduce the number of convergence steps. However, they typically incur significant computational overhead due to the additional losses from auxiliary tasks. To simultaneously achieve the enhanced capabilities of auxiliary training with the simplicity of standard SFT, we decouple the two objectives of auxiliary task training within the parameter space, namely, enhancing general capabilities and fitting task-specific action distributions. To deliver this goal, we only need to train the model to converge on a small-scale task set using two distinct training strategies. The difference between the resulting model parameters can then be interpreted as capability vectors provided by auxiliary tasks. These vectors are then merged with pretrained parameters to form a capability-enhanced meta model. Moreover, when standard SFT is augmented with a lightweight orthogonal regularization loss, the merged model attains performance comparable to auxiliary finetuned baselines with reduced computational overhead. Experimental results demonstrate that this approach is highly effective across diverse robot tasks. Project page: https://chris1220313648.github.io/Fast-dVLA/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25661 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fast-dVLA: Accelerating Discrete Diffusion VLA to Real-Time Performance Song, Wenxuan Chen, Jiayi Chen, Shuai Wang, Jingbo Ding, Pengxiang Zhao, Han Qin, Yikai Zheng, Xinhu Wang, Donglin Wang, Yan Li, Haoang Robotics Computer Vision and Pattern Recognition This paper proposes a novel approach to address the challenge that pretrained VLA models often fail to effectively improve performance and reduce adaptation costs during standard supervised finetuning (SFT). Some advanced finetuning methods with auxiliary training objectives can improve performance and reduce the number of convergence steps. However, they typically incur significant computational overhead due to the additional losses from auxiliary tasks. To simultaneously achieve the enhanced capabilities of auxiliary training with the simplicity of standard SFT, we decouple the two objectives of auxiliary task training within the parameter space, namely, enhancing general capabilities and fitting task-specific action distributions. To deliver this goal, we only need to train the model to converge on a small-scale task set using two distinct training strategies. The difference between the resulting model parameters can then be interpreted as capability vectors provided by auxiliary tasks. These vectors are then merged with pretrained parameters to form a capability-enhanced meta model. Moreover, when standard SFT is augmented with a lightweight orthogonal regularization loss, the merged model attains performance comparable to auxiliary finetuned baselines with reduced computational overhead. Experimental results demonstrate that this approach is highly effective across diverse robot tasks. Project page: https://chris1220313648.github.io/Fast-dVLA/ |
| title | Fast-dVLA: Accelerating Discrete Diffusion VLA to Real-Time Performance |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.25661 |