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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.

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Install

Install the adapter’s dependencies and the Prime CLI:
The recommended workspace keeps local environment packages under ./environments:
From the workspace root, install a local environment by name. For an environment from the Environments Hub, authenticate and use its user/environment ID:

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Configure

Create a Verifiers EnvConfig TOML file. A minimal config selects a taskset:
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.

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Run

The integration is a rollout function under examples/experimental/verifiers; its launcher wires everything up:
The launcher selects the adapter with --rollout-function-path, turns off Miles prompt-data loading with --disable-rollout-global-dataset, and points VERIFIERS_CONFIG at the file. 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: Evaluation covers every task in the taskset. Training cycles the taskset and advances from the current Miles rollout ID when a run resumes.

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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.

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Limitations

--eval-interval works through the launcher, which evaluates the whole taskset at that interval. Miles asserts that eval datasets are configured whenever the flag is set, so the launcher passes a placeholder --eval-prompt-data naming the taskset and pointing at its EnvConfig; the adapter serves evaluation, so the built-in loader never opens that path. --partial-rollout is not supported. A Verifiers episode owns live harness and environment state and has no contract for resuming a partially executed episode. The adapter rejects this combination when it is constructed, before any episode runs. --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. The adapter rejects the corresponding CLI options at 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.