Fast-dVLA: Accelerating Discrete Diffusion VLA to Real-Time Performance
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 a...
| Authors | Wenxuan Song, Jiayi Chen, Shuai Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 March 2026 |
| Last updated at source | 7 April 2026 |
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