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# Reproduce the OraRL Environment OraRL uses one reproducible software stack for policy optimization and task-native evaluation. The released configuration was validated on NVIDIA H20/Hopper GPUs with CUDA 12.9 and includes both runtimes in this repository. ## Requirements - Linux with Conda available in `PATH` - NVIDIA GPU with a recent driver - CUDA 12.9-compatible hardware for the paper-matched environment CUDA 12.9 GA officially requires NVIDIA Linux driver 575.51.03 or newer. Other NVIDIA GPUs may work, but the published setup was validated on H20. ## Install the pinned stack ```bash bash scripts/create_conda_env.sh conda activate orarl python scripts/check_environment.py \ --require-gpu \ --model /path/to/local/model ``` The installer pins Python 3.11, PyTorch 2.10.0+cu129, Transformers 5.5.4, vLLM 0.19.1, FlashAttention 2.8.3, and the remaining packages in `requirements-cu129.txt`. The final editable install exposes the `orarl-*` commands and the bundled `verl` trainer package from this checkout; do not install an unrelated `verl` release from PyPI over it. ## Validate an evaluation-only node If a node will only run evaluation, skip the training-side checks: ```bash bash scripts/create_conda_env.sh --evaluation-only conda activate orarl python scripts/check_environment.py --evaluation-only ``` ## Verify the release checkout Run the release checks before allocating a long job: ```bash python scripts/check_release.py python -m pytest -q ruff check . ``` On a paper-matched H20 node, add `--require-gpu --require-h20` to `scripts/check_environment.py`. Use the same source revision and environment on every node of a distributed run. Continue with [Training](training.md) or [Evaluation](evaluation.md).