rl-agentic-launch-iris

name	rl-agentic-launch-iris
description	Validate, submit, and observe agentic MarinSkyRL training on Iris when the configuration uses Harbor, Daytona, terminal-bench, or another sandboxed agent harness.

Launch agentic RL on Iris

Read .agents/ops/coreweave.md, the selected configuration, and the current cloud.iris.iris_backend interface before constructing a command. Resolve cluster, runtime profile, credentials, capacity, retry policy, and artifact destinations at execution time.

Workflow

1. Start from a clean committed revision and classify the harness from the resolved configuration.
2. Validate model, topology, rollout concurrency, sandbox provider, verifier, checkpoint policy, artifact paths, and campaign registration/publication policy.
3. Run the complete launch with --dry-run. Inspect the generated job identity, runtime commit and profile, resource roles, command, secret references, retries, and durable destinations.
4. Confirm external prerequisites using non-secret evidence: provider access, snapshot headroom, task data, model access, and target-cluster capacity.
5. Submit the reviewed command and preserve its resolved configuration with the experiment record.
6. Observe through initialization into completed trials, verifier rewards, advancing training steps, and a checkpoint when due. Use rl-job-health-deep-dive when state alone cannot prove progress.

Safety

• Never expose credentials in commands, logs, or artifacts.
• Never patch live pods or remote checkouts.
• Do not cancel or mutate a running job without authority.
• A running controller state without advancing trials or training is not proof of health.
• On failure, preserve the first causal evidence and recommend the smallest reproducible correction.