DexVLA
Midea Group, China · February 2025
Midea Group with East China Normal University and partners
- Parameters
- not published
- The paper names a Qwen2-VL-2B backbone and states that the diffusion expert is scaled to one billion parameters, but it never...
- GPU memory
- not published
- weights at bf16, computed
- Inference latency
- not published
- per action step
- Weights
- closed
- CC BY-NC 4.0.
What it is
DexVLA keeps a small Qwen2-VL-2B vision language model frozen in role and puts the capacity into the action side, a ScaleDP diffusion expert scaled to about one billion parameters that is pretrained on cross embodiment data before being attached. The three stage embodiment curriculum lets that expert be trained once and then aligned to a specific robot, which is what makes the model cheap to move between arms. It reports 97.3 percent average on LIBERO and completes long horizon tasks such as laundry folding from direct language prompting, and at about 60 Hz on a single A6000 it is by a wide margin the fastest model in this comparison. Two things constrain adoption: the licence is CC BY-NC 4.0, which rules out commercial deployment, and no full DexVLA checkpoint was ever published, only the pretrained diffusion experts, so the VLA has to be trained from the released code. The published LIBERO average also omits the Long suite, which is where most VLAs lose the most.
Architecture
- Backbone
- Qwen2-VL-2B-Instruct, used off the shelf with no post-training of the VLM itself
- Action head
- ScaleDP plug-in diffusion expert, released in two sizes: ScaleDP-H at about 1B parameters and ScaleDP-L at about 410M. The expert is pretrained separately on cross embodiment data before being attached to the VLM.
- Control rate
- 60 Hz
- Parameters
- The paper names a Qwen2-VL-2B backbone and states that the diffusion expert is scaled to one billion parameters, but it never gives a combined total. No official full DexVLA checkpoint is published from which a count could be read, only the two pretrained ScaleDP experts.
- Pretraining data
- About 100 hours of collected data spanning 91 distinct tasks across four embodiments, with AgileX contributing 42.7 percent and Franka 34.7 percent of the mix.
- Embodiments
- Franka Emika single arm, AgileX bimanual, Dexterous hand setup, LIBERO (simulation)
What hardware it needs
This is the section most people came for, so it is worth being precise about which numbers are measured and which are arithmetic.
| Fine tuning memory, reported | 38 GB |
| Control rate | 60 Hz |
Fine-tuning was tested on a single A6000 with 46 GB. Full training needs about 38 GB, LoRA about 24 GB and DeepSpeed offload about 20 to 29 GB, all at batch size 2. No inference VRAM number is published.
Reported results
Grouped by what the evaluation actually asked. Values from different suites are not comparable with each other, so each one keeps its suite and split.
Simulation
Reproducible benchmark suites. Everyone runs the same episodes, so the numbers can be compared inside a suite.
- LIBERO Object99.1%
- LIBERO average over Spatial, Object and Goal only97.3%success rateThe Long suite is not part of this average, so this number is not comparable with four suite LIBERO averages reported by other models. Same table: pi0 97.1, pi0-FAST 93.9, OpenVLA 84.1.source
- LIBERO Spatial97.2%success rateObtained with the stage 1 pretrained diffusion expert weights. Same table: pi0 96.8, OpenVLA 84.7, Diffusion Policy 78.3.source
- LIBERO Goal95.6%
Real world
No results in this category are published for this model.
Fine tuned tasks
No results in this category are published for this model.
On real hardware
Evaluated on Franka single arm, AgileX bimanual and a dexterous hand setup. The paper reports long horizon results in which DexVLA scores 0.4 on laundry folding against 0.2 for pi0 and 0.70 on the hard table bussing task against 0.62 for pi0, and it demonstrates direct language prompted shirt folding. Pretraining used about 100 hours of data across 91 tasks and four embodiments. Inference runs at about 60 Hz on a single A6000.
Fine tuning it yourself
Data is H5py in the ACT layout with left, right and wrist camera views plus joint positions and actions. Training is three staged: pretrain the diffusion expert alone on cross embodiment data, align the VLA to the target embodiment, then post-train on the target task. The README reports single A6000 runs at batch size 2 needing about 38 GB for full training, about 24 GB with LoRA, about 20 to 29 GB with DeepSpeed offload and about 33 GB with the smaller ScaleDP. Training only the diffusion expert runs at 0.89 epochs per hour versus 0.32 for the entire VLA.
Where it helps, where it does not
Strengths
- About 60 Hz on a single A6000, an order of magnitude faster than the diffusion VLAs that report around 5 Hz
- Real published training VRAM figures, including about 24 GB with LoRA, so fine-tuning fits on one 24 GB card
- The diffusion expert is separable and pretrained once, then aligned per embodiment, which cuts the cost of adding a new robot
- Demonstrated on single arm, bimanual and dexterous hand hardware, and on long horizon laundry folding from direct language prompting
Limits
- CC BY-NC 4.0 licence. Commercial use is explicitly forbidden, which disqualifies the model for most product work.
- No official full DexVLA checkpoint exists. Only the pretrained ScaleDP-H and ScaleDP-L diffusion experts are released, so the VLA must be trained from the code.
- The LIBERO average of 97.3 covers only the Spatial, Object and Goal suites. The Long suite, where VLAs typically lose the most, is not evaluated.
- No inference VRAM figure, no millisecond latency and no documented action chunk size, so the 60 Hz claim cannot be decomposed.
- Training data is H5py in the ACT layout, not RLDS and not LeRobot, so existing Open X-Embodiment pipelines need conversion.
- Pretraining is only about 100 hours across 91 tasks, small compared with the million trajectory corpora of foundation VLAs.
- Repository has been dormant since April 2025.
Sources
Everything on this page was taken from these documents. Where they disagree with what you read here, they win.
- https://arxiv.org/abs/2502.05855
- https://arxiv.org/html/2502.05855v3
- https://dex-vla.github.io/
- https://github.com/juruobenruo/DexVLA
- https://huggingface.co/lesjie/scale_dp_h
- https://huggingface.co/lesjie/scale_dp_l
Entry last checked 2026-08-11.
Try a policy on a real arm
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