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

# Environments

> How Miles trains on RL environments — datasets with rewards, self-wired environments, and optional external ecosystems.

Miles owns the training side of environment RL — batch orchestration, lossless
token-in/token-out recording, reward hooks, filtering — and is agnostic about
where the environment itself comes from:

* **No environment** — single-turn RLVR: a prompt dataset scored by the
  built-in rule-based rewards (math, ifbench, ...) or a custom reward function.
* **Your own environment** — plug your code into one of the three rollout
  layers described in [Integration shapes](#integration-shapes); most
  environments sit in the agent function, with the session server recording
  tokens (see [Rollout Endpoints](/docs/user-guide/rollout-endpoints)).
* **An external ecosystem** — adopt a prebuilt connector from the table below;
  connectors occupy the same three layers.

| Integration                                                                                        | Plugs in at       |
| -------------------------------------------------------------------------------------------------- | ----------------- |
| [Harbor](/docs/user-guide/harbor)                                                                       | agent function    |
| [OpenEnv](/docs/user-guide/openenv)                                                                     | agent function    |
| [NeMo-Gym](/docs/user-guide/nemo-gym)                                                                   | agent function    |
| [Strands Agents](https://github.com/radixark/miles/tree/main/examples/experimental/strands_sglang) | generate function |
| [τ-bench](https://github.com/radixark/miles/tree/main/examples/experimental/tau-bench)             | generate function |

Sandbox providers are a different axis: they provision the task containers
*inside* a connector rather than occupying a rollout layer.

| Sandbox provider                   | Used within               |
| ---------------------------------- | ------------------------- |
| [Daytona](https://www.daytona.io/) | OpenEnv, Harbor, NeMo-Gym |

All external ecosystem support is experimental.

## Integration shapes

The rollout stack is three nested plug-in layers (see
[Customization](/docs/user-guide/customization)): each column in the table below
wraps the one to its left, so replacing an outer layer also takes over
everything an inner one would. A connector replaces exactly one layer.

✓ = the external framework takes it over; ○ = stays in Miles.

|                                                        |   Agent function (innermost)   |         Generate function         | Rollout function (outermost) |
| ------------------------------------------------------ | :----------------------------: | :-------------------------------: | :--------------------------: |
| Plug-point flag                                        | `--custom-agent-function-path` | `--custom-generate-function-path` |   `--rollout-function-path`  |
| Agent–environment loop                                 |                ✓               |                 ✓                 |               ✓              |
| Trajectory & token recording                           |                ○               |                 ✓¹                |              ✓¹              |
| Reward pathway (RM hooks, group rewards)               |               ○²               |                 ○²                |               ✓              |
| Data source (prompts / taskset)                        |                ○               |                 ○                 |               ✓              |
| Batch orchestration (grouping, filtering)              |                ○               |                 ○                 |               ✓              |
| Model, engines & weight updates, advantages, optimizer |                ○               |                 ○                 |               ○              |

¹ Typically by speaking SGLang's native `/generate` (token IDs in and out)
rather than the session-server chat endpoint Miles' own recording uses.

² The environment may grade an episode itself (Harbor and τ-bench do); the
score still enters training through Miles' `Sample.reward` / RM hooks, and
group-level reward handling stays in Miles.