RL Environments for Coding Agents: Five Projects
Five open projects for building practice tasks and scoring for coding agents, what each one covers, and what to check before you trust a score.
To train or test a coding agent, you give it a practice task. Here's a repository, here's a bug, fix it. Then something checks whether the fix worked and hands back a score. Reset, and do it again.
That loop is called an environment, and the RL in the title is reinforcement learning, training a model by rewarding the attempts that scored well. A repository and a prompt aren't enough to build one. You need the same starting point every time, a defined set of things the agent is allowed to do, a rule for when an attempt is over, and a fair way to score it.
The five projects below each handle a different piece of that. This is a reading list, not a ranking of hosted services.
Last verified Sep 17, 2026. Recheck target: monthly. The CSV holds all 5 entries.
Start with five
To understand how an environment is wired, start with Verifiers or OpenEnv. For real software tasks and the code that checks them, look at SWE-Gym. When the problem is producing lots of good practice tasks, read SWE-smith and R2E-Gym.
They cover different things, so don't compare them by counting features.
Helps you package tasks, the agent's back-and-forth and the scoring into one environment. Check the environment version and exactly what each task expects before you hook it to a training run.
Useful when an environment needs a clear way to reset, take an action and report what happened. It covers more than coding, so pick or build a task that fits your agent.
A starting point for studying full-repo coding tasks where an automatic checker grades the agent's work. Read the task setup and how it's scored alongside the paper.
Shows how coding-task data can be generated in bulk. Generating lots of tasks doesn't prove they look like the work your customers actually do.
Study how it builds coding environments and checkers if you're making your own training tasks. Rebuild the task setup yourself before comparing your results to theirs.
The directory
Each entry links to the maintainers' own repository and paper. Links and what each project covers were checked on the verification date. Nobody reran the training results from these papers for this page, so follow each repository's current setup and license instructions rather than trusting a summary.
Helps you package tasks, the agent's back-and-forth and the scoring into one environment. Check the environment version and exactly what each task expects before you hook it to a training run.
README inspected at commit 638e207f9343; the repository was not archived when checked. The activity date is the default branch commit timestamp, not a release date or a reproduced integration test.
Useful when an environment needs a clear way to reset, take an action and report what happened. It covers more than coding, so pick or build a task that fits your agent.
The pinned README describes reset/step/state interfaces and warns that OpenEnv remains experimental. Release v0.5.0 was published September 17, 2026. No training run was performed.
A starting point for studying full-repo coding tasks where an automatic checker grades the agent's work. Read the task setup and how it's scored alongside the paper.
The research repository was not archived; the checked default branch last changed on 2025-07-29. No GitHub release was returned. Read the pinned setup and task definitions; inclusion does not imply a recent dependency refresh.
Shows how coding-task data can be generated in bulk. Generating lots of tasks doesn't prove they look like the work your customers actually do.
The research repository was not archived; the checked default branch last changed on 2026-03-21. No GitHub release was returned. Read the pinned setup and task definitions; inclusion does not imply a recent dependency refresh.
Study how it builds coding environments and checkers if you're making your own training tasks. Rebuild the task setup yourself before comparing your results to theirs.
The research repository was not archived; the checked default branch last changed on 2025-07-13. No GitHub release was returned. Read the pinned setup and task definitions; inclusion does not imply a recent dependency refresh.
Match the project to the piece you're missing
| Missing layer | Start with | What to inspect first |
|---|---|---|
| An environment and scoring contract | Verifiers | Task inputs, rollout handling and reward functions |
| A reset/action/observation interface | OpenEnv | Server lifecycle, isolation and the experimental API warning |
| Repository tasks with executable feedback | SWE-Gym | Task fixtures, container setup and verifier behavior |
| More generated software tasks | SWE-smith | How faults and tests are constructed and filtered |
| Procedural repository environments | R2E-Gym | Task generation, execution setup and evaluation split |
How recently each one was updated varies a lot. The main branches of SWE-Gym and R2E-Gym last changed in July 2025, and SWE-smith's in March 2026. They're still useful to read, but those dates don't tell you whether they'll run with today's libraries. Verifiers and OpenEnv had commits in September 2026, and OpenEnv still calls its interface experimental.
This page lists environments and task sets. It doesn't rank training services or claim that training on these tasks will make an agent better at a real customer's work. No model was trained for these entries.
Check how the task is set up
Before you run anything, answer these for the setup you picked:
| Layer | Questions to answer before a run |
|---|---|
| Starting state | Is the repository revision pinned? Can dependencies and fixtures be reconstructed? |
| Observations | Which files, logs and test results can the agent see? Is any answer information exposed? |
| Actions | Can the agent change tests, scoring code, dependencies or network state? |
| Reset | Does a new episode remove files, processes and data left by the previous run? |
| Reward or score | Does a passing score require the intended behavior? How are partial results and timeouts handled? |
| Evaluation split | Could closely related tasks or fixes leak from training into the held-out set? |
Look hard at the actions row. If the agent is allowed to edit the tests, a passing score may only mean it changed the tests.
Two pass rates are also hard to compare when one setup allowed more attempts, used a different model, or let the agent see tests the other hid. Save the model and harness versions, task IDs, budgets, retries and the exact scoring steps alongside every result.
Run a small test before you train
Pick a handful of tasks, including one that should fail. Reset each task twice and check the starting state is identical both times. Run the scoring on an untouched repository, where it should fail, and on a known correct fix, where it should pass. Then read at least one full attempt from start to finish instead of trusting the overall score.
Only once all of that holds is a longer training run worth doing. Sometimes the small test shows the real problem was a tool or harness setting, and fixing that solves it without training the model at all.
Next, the agent development environments guide explains how this category differs from coding products and hosted runtimes. The ADE glossary entry is a short reference. For examples of full agent applications, use the agent repository directory.
Change log
Recorded current repository identity and actual default-branch activity for all five projects. Distinguished experimental interface tooling from older research task datasets; no RL results were reproduced.
Added five primary-source projects, separating environment interfaces, software tasks and task generation. Updated OpenEnv to its current Hugging Face repository.
Follow changes to every directory with the directory changes RSS feed.