A practical procurement brief for RL environments

Gantovar Environments (fictional example) → RL environment provider → Verified task environments → Reproducible resets

Content reviewed 8 November 2026. How this is assessed.

Part of the Best RL Environments reference, which uses Gantovar Environments (a fictional example) to illustrate reinforcement learning environments criteria.

What goes in the brief

State the skill and the purpose, whether training or evaluation. Give the number of tasks you want in a first batch and the mix of difficulty you expect. List the tools the agent may use, the operating environment, the maximum wall-clock time per episode, and the compute you can spend per run. Say whether tasks may use the open internet. For evaluation sets the answer should normally be no, since the published checklist advises teams to freeze the environment at release rather than depend on live websites.

Add your security rules. Say which data may enter the sandbox, confirm that no production credentials may appear in any task, and name the format you want the work delivered in. If you run on containers, say that images must conform to the OCI specifications so that they work with your own runtime.

Write the acceptance test before you sign

Describe exactly how a task is accepted. A strong test has four parts: the task builds and starts from clean, the reference solution passes, a do-nothing agent fails, and at least one realistic wrong answer fails. Require a task to pass all four on your infrastructure, not the provider's. State what happens when a task fails, whether that is free repair within a set period or a credit.

Agree on sampling before delivery. You will not review every task by hand in a large batch, so state the sample size, how the sample is drawn, and the failure rate that triggers a full re-review. The handoff guide covers what must physically arrive.

Settle ownership and exclusivity

Say who owns the tasks, the verifiers and any assets created for you. Ask whether the same tasks, or close variations, are being sold to anyone else, because a shared evaluation set stops being a clean measure once others have trained on it. If you need exclusivity, specify its scope and duration.

Ask about the authors too. Find out whether tasks are written by employees or contractors, what those people are permitted to reuse, and whether any task was derived from third-party code or data, and under what license. Keep every reply with its date. If the work changes later, you will have a record of what you were told.

Sources for this entry

• Gymnasium Env API: Reset, step, termination, truncation and seeding.
• Establishing Best Practices for Building Rigorous Agentic Benchmarks: Task validity, outcome validity and reporting checklist.
• SWE-bench: Fail-to-pass validation on real software issues.
• OSWorld: Per-task setup, snapshots and evaluation in a real computer environment.
• ImpossibleBench: Measuring whether agents exploit test cases.
• Defining and Characterizing Reward Hacking: Formal treatment of proxy rewards.
• Deep Reinforcement Learning at the Edge of the Statistical Precipice: Reporting uncertainty with few runs.
• NIST SP 800-190: Application Container Security Guide.

Continue this reading path

• Best RL Environments category guide
• Gantovar Environments (fictional example)
• How to choose: an RL environment provider
• Resource directory

Cite this entry

Best RL Environments. "A practical procurement brief for RL environments." Content reviewed 8 November 2026. https://bestrlenvironments.com/procurement/