At launch
- Script-based farm workflows, evidence and customer controls.
- Optional example collection during normal VRA runs.
- Default-off consent, privacy controls, review and withdrawal.
- A contribution pipeline ready before customer release.
OUR RELEASE ROADMAP
Scripts first.
A better model must earn its place.
Launch with controlled automation. Build the dataset with permission. Release our experimental model only when internal testing shows an improvement—not simply because it uses AI.
VRA is pre-release. This is the plan for launch and the later experimental-model iteration, not a claim that either is available to download today.
In a later iteration
- Young Studio’s own experimental VRA vision/language model.
- Training on developer examples and permitted customer contributions.
- Internal comparison against the script-based baseline.
- Release only after demonstrated improvement and qualification.
Scripts establish the baseline
The initial release will use premade, deterministic workflows. They record what was observed, what was attempted and what was verified. An attempted action without a confirmed result is not a successful demonstration.
Customers choose whether to contribute
At launch, optional dataset contribution is part of the release plan: with explicit opt-in, VRA will select useful examples during normal automation, apply privacy controls and contribute approved examples to Young Studio’s VRA dataset. Manual screenshot preparation will not be required for everyday contribution.
We build and train the VRA model
We are building Young Studio’s own VRA model. We will train and evaluate it using teacher-labelled development examples and optional contributions from customers running VRA. Customers contribute examples—not train or maintain separate models themselves. Training and evaluation happen offline—not on the customer’s PC during a farm run.
The model must prove an improvement
We will compare candidate model versions against the script-based baseline on held-out examples and internal end-to-end runs. Completing work, handling uncertainty, respecting permissions and avoiding unintended actions matter more than a persuasive prediction.
Experimental release comes later
The experimental VRA vision/language model is planned for a later iteration—not the initial launch. We will release it only after internal testing demonstrates an improvement over the script-based automation baseline. Contributions do not immediately change a live farm’s behaviour. Qualified versions will arrive through VRA updates.
Your permissions remain authoritative
Whether a workflow uses scripts or a qualified model, spending permissions, workflow limits and replay guards remain the control boundary. A model cannot grant itself permission or turn uncertainty into a completed result.
YOUR DATA IS NOT THE DEFAULT
Help improve VRA.
Only if you choose.
The model will be ours. The decision to contribute examples will remain yours. Declining will not reduce core farm automation.