> ## Documentation Index
> Fetch the complete documentation index at: https://docs.reactor.inc/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> To build and serve your own model, start at /deploy/development/quickstart and /deploy/development/overview. Deploying is the default path: reactor init scaffolds a workspace, reactor auth login authenticates, and reactor model deploy registers the model, publishes the release with the weights/ folder, and activates it on Reactor's GPUs, in one command from that workspace. Docker must be running, because the publish step builds the image locally. Bump model.version in reactor.yaml before redeploying a change, because a release that already has an image is reactivated as it is. Deployment access is granted per account, so contact team@reactor.inc if a deploy is refused. Every key in reactor.yaml is documented at /deploy/platform/reactor-yaml. Model code imports reactor_runtime; Python client code imports reactor_sdk. The runtime overview explains the model interface. Running the model on your own machine with reactor run is optional and needs a GPU you attach with --gpus; /deploy/development/local-testing covers that loop and pairs a complete brightness model with a Python client test in a separate brightness-test workspace.
> Reactor hosts multiple models, each with its own connect slug (modelName) and command/event schema. The catalog of every model — slug, typed SDK package, and links to its schema — is at /model-api-reference/overview. Some models expose one slug per experience (e.g. HappyOyster); always take the slug from the model's own pages, never guess it.
> Fastest path to a working app: `npx create-reactor-app my-app --model=<slug>` scaffolds a complete app with secure auth wired up. Typed TypeScript SDKs are published as @reactor-models/<model>; Python uses the base reactor-sdk package.
> Auth: exchange an API key (rk_...) for a JWT via POST https://api.reactor.inc/tokens from your server. Never put the API key in client-side code.
> Append .md to any docs URL for clean Markdown. Search these docs via the MCP server at https://docs.reactor.inc/mcp.

# Deploy RLDX-1

> Deploy the RLDX-1 three-camera vision-language-action model on Reactor.

RLDX-1 is a vision-language-action (VLA) model by [RLWRLD](https://github.com/RLWRLD/RLDX-1). It
reads three camera views, robot state, and a task description. It returns chunks of actions. It does
not generate video.

In this guide, you deploy RLDX-1 on Reactor using a prebuilt model image. Then you test the
deployment with a Python client that sends synthetic camera frames and robot state.

The
[complete example in the Reactor cookbook](https://github.com/reactor-team/reactor-cookbook/tree/main/models/rldx-1)
includes the model code, deployment configuration, and test client. This example enables guided
Real-Time Chunking (RTC). The model uses three 256 × 256 RGB views and returns 16-step action chunks
over the data channel.

## Requirements

You need Git, a running Docker engine, and a Reactor account with deployment capacity in your
selected region. Your laptop does not need an NVIDIA GPU.

Your account needs deployment access. [Contact us](mailto:team@reactor.inc) if your account does not
have access.

The cookbook code is Apache-2.0. The checkpoint uses the separate
[RLWRLD Model License](https://huggingface.co/RLWRLD/RLDX-1-FT-ROBOCASA/blob/main/LICENSE.md).
Review its use restrictions before you download or deploy the weights.

## Deploy the model

### 1. Clone the cookbook

```bash theme={"theme":{"light":"github-light","dark":"github-dark-high-contrast"}}
git clone https://github.com/reactor-team/reactor-cookbook.git
cd reactor-cookbook
```

### 2. Install the CLI and sign in

```sh theme={"theme":{"light":"github-light","dark":"github-dark-high-contrast"}}
curl -fsSL https://reactor.inc/install | bash
```

```bash theme={"theme":{"light":"github-light","dark":"github-dark-high-contrast"}}
reactor version
reactor auth login
```

Complete the browser sign-in with the account that will own the deployment. See
[Install the CLI](/deploy/platform/installation) for installation options and
[Authenticate](/deploy/platform/authentication) for CLI sign-in details.

### 3. Pull the model image

Start Docker. Pull the published image:

```bash theme={"theme":{"light":"github-light","dark":"github-dark-high-contrast"}}
docker pull reactortechnologies/rldx-1:1.0.1
```

Keep the image tag `1.0.1` when you publish. This image tag is separate from the model release. The
CLI reads `model.name` and `model.version` from `reactor.yaml`.

### 4. Open the model workspace

```bash theme={"theme":{"light":"github-light","dark":"github-dark-high-contrast"}}
cd models/rldx-1
```

The workspace points to the public Hugging Face checkpoint:

```yaml theme={"theme":{"light":"github-light","dark":"github-dark-high-contrast"}}
runtime:
  weights_path: "hf://RLWRLD/RLDX-1-FT-ROBOCASA"
```

The CLI records the pinned reference. Reactor fetches the checkpoint when the model starts.

The example `reactor.yaml` requests one instance in `us-west` by default. If you use it unchanged,
your account needs capacity in `us-west`. To use another region, change `region` under
`deployment.instances` in `reactor.yaml` before you deploy.

### 5. Publish, deploy, and check status

Run each command from `models/rldx-1`.

```bash theme={"theme":{"light":"github-light","dark":"github-dark-high-contrast"}}
reactor model publish --source reactortechnologies/rldx-1:1.0.1
reactor model deploy
reactor model status
```

`publish --source` pushes the downloaded image into your account without rebuilding it. It
associates the checkpoint with the release. `deploy` activates the release and applies the instance
plan.

Wait for the deployment to become ready. Startup includes fetching the checkpoint and loading the
model. Keep the account-qualified model name that the CLI prints, such as `your-account/rldx-1`.

Next, [test your deployment](/deploy/development/robotics/test-client) with the matching Python
client. For the deployment workflow, see [Deploying models](/deploy/platform/deploying). For
checkpoint options, see [Load your weights](/deploy/development/weights). The CLI reference covers
[publish](/deploy/cli-reference/reactor_model_publish),
[deploy](/deploy/cli-reference/reactor_model_deploy), and
[status](/deploy/cli-reference/reactor_model_status).