# laion/wizardlm-orca-v2

LLM-judge-verified version of `DCAgent/wizardlm-orca-sandboxes`.

Each task follows the `nemotron_gym` LLM-judge verifier contract:

* `instruction.md` — the original task instruction, with submission guidance
  appended directing the agent to write its answer to `/app/response.txt`.
* `tests/test_state.py` — a pytest-runnable LLM judge that reads
  `/app/response.txt` (legacy `/app/answer.txt` fallback), reads
  `tests/verifier_data.json` for the instruction + rubric, calls
  `litellm.completion` (default `openai/gpt-4o-mini`, temperature=0) with the
  rubric, parses `\boxed{<score>}`, and writes the float score to
  `/logs/verifier/reward.txt`.
* `tests/test.sh` — defaults reward to 0, then runs
  `python3 -m pytest /tests/test_state.py`.
* `tests/verifier_data.json` — the task instruction + a per-dataset rubric.
* `environment/Dockerfile` — `ubuntu:24.04` + python3 + pip + openai + pytest
  + litellm.
* `task.toml` — `LLM_JUDGE_TASK_TOML`, so `OPENAI_API_KEY` / `JUDGE_MODEL`
  propagate into the verifier container via `[verifier].env` (`OPENAI_API_KEY`
  is required at trial time).

General instruction-following tasks (system + instruction from the
WizardLM_Orca source). Graded by an LLM judge on correctness /
completeness / relevance.

**Rubric:** correctness / completeness / relevance

`10000` tasks.