rlenv-audit 0.3.4

rlenv_audit, a skill-based auditing system for Prime Intellect `verifiers` RL environments

rlenv_audit

rlenv_audit audits verifiers RL environments from the Prime Intellect Hub before you spend GPU hours training on them. RL environments are treated like training data, but nobody tests them first: a broken reward function doesn't crash, it silently teaches the policy garbage. rlenv_audit catches that. Point an agent (Claude Code / Codex) at an environment and it runs six judgment-based checks (each a skill file the agent executes, backed by a small deterministic tool layer) and returns a scorecard with a score out of 10, a status, and a written justification per check, plus overall feedback on what the env does right and what to improve.

Quickstart

# Install the skills (pick one)
uvx rlenv-audit install-skills
pip install rlenv-audit && rlenv-audit install-skills

Then ask your agent, giving the full environment id (account/name; bare names like gsm8k are ambiguous on the Hub), your problem statement, and optionally a model endpoint and the HuggingFace datasets to check contamination against:

prompt

Audit primeintellect/gsm8k. I'm trying to train a grade-school math solver.
Use my vLLM endpoint at http://localhost:8000/v1, model Qwen2.5-7B.
Check contamination against openai/gsm8k.

(in Claude Code or Codex)

Output

The scorecard, one row per check, each scored out of 10, plus one final score and written feedback:

                       rlenv_audit · primeintellect/gsm8k
┃ check             ┃ status ┃ score ┃ justification                           ┃
│ integrity         │ PASS   │   9.5 │ loads, reward callable, well-formed     │
│ problem_alignment │ PASS   │   9.0 │ dataset/reward match the stated goal    │
│ reward_design     │ PASS   │   8.8 │ discriminates; matches judgment 18/20   │
│ latency           │ N/A    │     — │ no endpoint                             │
│ rollout_quality   │ N/A    │     — │ no endpoint                             │
│ contamination     │ WARN   │   6.0 │ 3 near-matches with openai/gsm8k test   │
overall: WARN   rating: 8.7/10

feedback
The environment is solidly built: it loads cleanly, the reward is a real
verifier (boxed-answer extraction + math equivalence, not a stub), and it
discriminates well: correct completions scored 1.0 and every wrong or
malformed probe scored 0.0, matching my own judgment on 18 of 20 cases.

The main thing to improve is contamination: 3 of the sampled training
instances near-match the openai/gsm8k test split you asked me to check, so
benchmark gains may partly be memorization; either dedupe against that test
split or report on a different set. Second, the parser only accepts \boxed{}
answers; consider
accepting plain final-line answers too, or the policy gets zero reward for
correct-but-unformatted output early in training.

• Final score: a weighted average out of 10 over the checks that ran (N/A carries no weight). Latency and contamination weigh 0.5 each, the other four checks 1.0.
• Feedback: 1 to 3 paragraphs, what the env does right first, then what to improve, in priority order.
• A FAIL on any check fails the audit.

The six checks

#	Check	Needs	What it does
1	integrity	-	Does it even run and is it shaped right: dataset loads & is well-formed, reward present & callable, follows verifiers conventions, no missing fields / broken imports.
2	problem-statement alignment	-	Given your problem statement (a required input), judge whether the dataset + reward + prompt actually test that problem.
3	reward design	-	Stress-tests the reward without the policy: the agent writes ~20 synthetic completions (correct / wrong / edge / format perturbations), scores them through the real reward, and checks (a) the reward varies & discriminates sensibly and (b) each reward matches the agent's own judgment of quality.
4	latency	model endpoint	How long rollouts take end to end. Reads the shared cached rollouts.
5	rollout quality	model endpoint	Reads actual rollouts and judges whether the env is set up well in practice: system prompt right, outputs sensible, obvious env-caused failure modes.
6	contamination	HF dataset ids	Compares the env's dataset against the HuggingFace datasets you name (e.g. openai/gsm8k) and flags matching / near-matching instances. N/A (carries no weight) if you don't provide any.

Shared rollouts (checks 4 & 5). Both need a model, so rlenv_audit runs rollouts once (8 rollouts over ~20 samples, scored + timed, cached) and both checks read that single cache. No endpoint → 4 & 5 are N/A.

Layout

skills/                 the six checks + the env-audit orchestrator (SKILL.md each)
.claude-plugin/         plugin + marketplace manifests (repo doubles as a Claude Code plugin)
rlenv_audit/
  adapters/verifiers.py EnvHandle, the only code that touches verifiers
  tools.py              inspect / score / rollouts / scorecard
  sandbox.py            Docker isolation (for executing risky completions)
  cli.py                the rlenv-audit / env-audit CLI (+ install-skills)
REWARD_DESIGN.md        the design guide the judgment checks cite

Development

pip install -e ".[dev]" && pytest tests/

License

MIT