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

# Artifacts

> Self-describing policy directories. Save once, load anywhere.

A saved policy is a directory, not a pickle. Every artifact bundles weights,
architecture, robot config, and normalizer state so it can be loaded with
zero context on another machine.

## Layout

```text theme={null}
runs/go2-walk-v1/
├── rfx_config.json     # architecture + robot + training metadata
├── model.safetensors   # weights
└── normalizer.json     # observation normalizer state
```

## Save

```python theme={null}
policy.save(
    "runs/go2-walk-v1",
    robot_config=config,
    normalizer=normalizer,
    training_info={"total_steps": 50000},
)
```

## Load

```python theme={null}
loaded = rfx.load_policy("runs/go2-walk-v1")
loaded = rfx.load_policy("hf://rfx-community/go2-walk-v1")

loaded.policy         # reconstructed policy
loaded.robot_config   # RobotConfig or None
loaded.normalizer     # normalizer or None
loaded.policy_type    # "MLP", "ActorCritic", ...
```

`LoadedPolicy` is callable and handles torch / tinygrad conversion
automatically.

## Push and pull via HuggingFace Hub

```python theme={null}
rfx.push_policy("runs/go2-walk-v1", "rfx-community/go2-walk-v1")
loaded = rfx.load_policy("hf://rfx-community/go2-walk-v1")
```

Policies travel the same way datasets do.

## Inspect without loading

```python theme={null}
config = rfx.inspect_policy("runs/go2-walk-v1")
print(config["policy_type"])    # "MLP"
print(config["policy_config"])  # {"obs_dim": 48, ...}
```

<Tip>
  `rfx deploy` calls `load_policy` internally. If the artifact's
  `rfx_config.json` records a robot, `--robot` is inferred automatically.
</Tip>