Task design 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.

Write the task as a contract

Each task should say what the agent starts with, what it may do, how long it has, and what the finished state looks like. The published Agentic Benchmark Checklist advises naming the required tool and package versions in the prompt and making sure each task can be solved only by agents that have the target capability. Read each instruction as a new employee would. If two reasonable people could produce different correct results, either the task needs tightening or the verifier must accept both.

The size of the state space matters as well. Real benchmark design shows the range of what is possible. SWE-bench draws 2,294 problems from real issues and their pull requests across 12 popular Python repositories, which forces edits across several functions and files. OSWorld builds 369 computer tasks spanning applications, file handling and multi-application workflows, using 302 distinct initial states.

Include tasks that should be refused

A set made only of solvable tasks rewards an agent that always tries and always claims success. OSWorld includes 30 infeasible tasks, about 8.1 percent of its set, built around deprecated or invented features, and gives credit for correctly declaring that the task cannot be done. Consider a similar slice if your agent will meet impossible requests in real use.

Spread difficulty deliberately. Include easy tasks that a working agent should nearly always finish, since consistent failure on easy tasks suggests a broken task. The checklist makes this point directly, advising teams to inspect outliers in pilot runs, because failure on easy tasks can indicate impossible tasks and success only on hard tasks can indicate shortcuts.

Avoid the common design faults

Watch for tasks that leak the answer in a file name, a commit message, a comment or a directory listing. Watch for tasks whose difficulty lies in guessing an unstated output format rather than in doing the work. Watch for near-duplicates: thirty tasks generated from one template teach one thing thirty times, and they inflate the count without adding coverage.

Keep a held-out set that is never used in training, and never let its contents appear in training prompts. Record which template, author and source each task came from so that you can split by family rather than by individual task. Without that, near-duplicates can appear on both sides of the split, and your held-out score will overstate what the agent can do on work it has not seen. The evaluation guide continues from here.

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. "Task design for RL environments." Content reviewed 8 November 2026. https://bestrlenvironments.com/task-design/