Envsmithy · RL environments & training data
Environments,
forged at scale.
Verifiable RL environments and the training data that goes with them. Executable tasks, graders tested against wrong answers, trajectories with provenance.
Environment suites
Three things, made to be trained on
Most available environments are either toy-sized or can't be checked. We build the third kind: realistic, verifiable, and released as suites.
Environments
Verifiable tasks
Each task carries an initial state, a tool interface, a success specification, validated solution traces, and an executable grader tested on valid, invalid, and near-miss outcomes.
Data
High-quality trajectories
Timestamped tool-use trajectories and pairwise preference records. Each sample carries annotator qualifications, task and environment versions, and review decisions.
Scale
Suites, not one-offs
Parameterised task variants generated from a domain spec, filtered by automated and expert checks, with difficulty estimated from success rates across named model-and-scaffold baselines.
Domains
Supported domains
Long-horizon work where success can be checked against state, not judged from the transcript.
Coding
Repository tasks graded on held-out functional tests, regression tests, and forbidden side effects. Patch similarity is not part of the reward.
Computer & browser use
Multi-step tasks in real applications, verified on the resulting application state rather than the action sequence.
Data engineering
Pipelines, schemas, and messy inputs. Outputs checked against ground truth; runs must be reproducible.
Engineering design
Design tasks graded against explicit constraints with simulation or optimisation, plus expert review for the assumptions a solver doesn't capture.
Research
What every task is held to
Environment design is a research problem. Three properties are non-negotiable.
# fields every task record carries task id · domain · spec version state seeded initial state · deterministic reset tools shell · python · domain simulator grader deterministic state checks + rubric dimensions validation reference traces pass · near-miss + exploit probes fail baseline pass@1 per named model + scaffold, N trials provenance author · reviewers · review decisions
Record schema · values are placeholders, not measurements
Verifiable
Reward is decided by state checks or a solver. Rubric dimensions only where judgement is unavoidable, with inter-rater agreement measured and reported.
Non-trivial
Tasks solvable without interacting with the environment are rejected. Prompt-only and tool-enabled baselines are both reported.
Hack-resistant
Graders are tested against near-miss and exploit attempts before release, so the shortest path to reward is the intended one.