feat: add Verifiers rollout integration

docs/docs.json

@@ -169,6 +169,7 @@
        "user-guide/monitoring",
        "user-guide/customization",
        "user-guide/rollout-endpoints",
+       "user-guide/verifiers",
        "user-guide/fully-async",
        "user-guide/agentic-chat-template",
        "user-guide/cli-reference"

docs/user-guide/cli-reference.md

@@ -191,6 +191,15 @@ Sections mirror the launch-script argument groups.
 | `--rollout-stop` | str+ | – | Stop strings. |
 | `--rollout-stop-token-ids` | int+ | – | Stop token IDs. |
 
+### Rollout: Verifiers
+
+| Flag | Type | Default | Notes |
+|---|---|---|---|
+| `--verifiers-config` | path | – | Use the built-in Verifiers rollout with this EnvConfig TOML file. |
+
+See [Verifiers](/user-guide/verifiers) for installation, configuration, and supported
+environment behavior.
+
 ### Eval
 
 | Flag | Type | Default | Notes |

docs/user-guide/index.md

@@ -11,6 +11,7 @@ description: Concepts, launch script walkthrough, customization hooks, and a com
 | [Monitoring & Logging](/user-guide/monitoring) | wandb, structured logs, per-source breakdowns, profiling, router metrics. |
 | [Customization](/user-guide/customization) | The 21 `--*-path` plug-points for custom Python — rollout, reward, filters, loss, hooks. |
 | [Rollout Endpoints](/user-guide/rollout-endpoints) | The `/generate` endpoint and the OpenAI chat endpoint for agentic sessions. |
+| [Verifiers](/user-guide/verifiers) | Train on Verifiers tasksets and harnesses. |
 | [Fully Async Rollout](/user-guide/fully-async) | Queue-backed rollout production, tuning knobs, and when to use `train_async.py`. |
 | [Agentic Chat Templates](/user-guide/agentic-chat-template) | Turning on and verifying TITO so multi-turn agentic rollout stays append-only. |
 | [CLI Reference](/user-guide/cli-reference) | Every flag Miles accepts, grouped by subsystem. |

docs/user-guide/verifiers.md

@@ -0,0 +1,111 @@
+---
+title: Verifiers
+description: Train on Verifiers environments with Miles.
+---
+
+Miles can train on a Verifiers environment in place of a prompt dataset. The
+integration requires Python 3.11 or newer and Verifiers 0.2.0. Verifiers 0.2.1
+requires OpenAI 2.9 or newer, while SGLang 0.5.15 pins OpenAI 2.6.1.
+
+## Install
+
+Install the optional dependencies with Miles and install the Prime CLI:
+
+```bash
+pip install -e '.[verifiers]'
+uv tool install prime
+```
+
+The recommended workspace keeps local environment packages under `./environments`:
+
+```text
+workspace/
+  environments/
+    my-environment/
+```
+
+From the workspace root, install a local environment by name. For an environment from
+the Environments Hub, authenticate and use its `user/environment` ID:
+
+```bash
+# Local: ./environments/my-environment
+prime env install my-environment
+
+# Environments Hub
+prime login
+prime env install user/my-environment
+```
+
+## Configure
+
+Create a Verifiers `EnvConfig` TOML file. A minimal config selects a taskset:
+
+```toml
+[taskset]
+id = "gsm8k-v1"
+```
+
+The config may also define the harness, runtime, judges, retries, and environment
+limits supported by Verifiers. Verifiers applies per-rollout and group rewards before
+the completed traces are returned to Miles.
+
+The integration implements Verifiers' V1 environment contract. Legacy V0 environment
+configs are rejected during startup.
+
+## Run
+
+Add one option to a normal Miles training command:
+
+```bash
+--verifiers-config /path/to/verifiers.toml
+```
+
+This uses the configured taskset instead of Miles prompt data. Environment behavior
+comes from the Verifiers config, while Miles continues to own the model, sampling,
+batching, concurrency, reward hooks, and optimizer settings. The Renderers library
+formats environment messages with Miles' model and tokenizer settings.
+
+The standard Miles rollout options keep their existing meaning:
+
+| Miles option | Verifiers behavior |
+|---|---|
+| `--rollout-batch-size` | Number of task groups returned by each training rollout |
+| `--n-samples-per-prompt` | Rollouts per training task |
+| `--n-samples-per-eval-prompt` | Rollouts per evaluation task |
+| `--rollout-shuffle` / `--rollout-seed` | Finite taskset order and sampling seeds |
+| `--rollout-*` / `--eval-*` sampling options | Sampling and context limits |
+| `--apply-chat-template-kwargs` | Typed template options passed to renderers |
+| `--sglang-server-concurrency` | Physical engine capacity |
+| Miles reward and filtering options | Applied after Verifiers scoring using the standard Miles hooks |
+
+Evaluation covers every task in the taskset. Training cycles the taskset and advances
+from the current Miles rollout ID when a run resumes.
+
+## Environment Support
+
+The adapter supports V1 environments that use the Chat Completions dialect with
+text-only Renderers inputs. Tools require a model-specific renderer; use a registered
+model identity in `--hf-checkpoint` or the existing `--sglang-tokenizer-path` option.
+User simulators, multi-turn episodes, environment runtimes, per-rollout rewards, and
+group rewards run through the standard Verifiers environment lifecycle.
+
+Verifiers group rewards apply during both training and evaluation. Miles
+`--group-rm` hooks remain training-only, matching the standard Miles rollout path.
+
+## Limitations
+
+`--partial-rollout` is not supported. A Verifiers episode owns live harness and
+environment state and has no contract for resuming a partially executed episode. Miles
+rejects this combination during argument validation.
+
+`--chat-template-path` is also rejected because Renderers owns message formatting for
+Verifiers environments. Use the checkpoint's native template and
+`--apply-chat-template-kwargs` instead.
+
+Streaming model requests, Responses and Anthropic dialects, multimodal inputs, OPD,
+routing replay, and indexer replay are not supported by the transport. Miles
+rejects the corresponding CLI options when they can be detected during startup.
+
+Traces with multiple graph branches, including compaction, are rejected. Miles does
+not currently preserve a trace's rollout-group boundary when it flattens multiple
+training samples, which would make group-relative advantages incorrect.