Reproducibility Assurance Program
Open-Reasoner-Zero provides full reproduction support:
- Docker full environment package::
- Pre-built images:
docker pull openreasonerzero/official:latest - Precise version control: Dockerfile locks PyTorch 2.0.1 + cu117 and other core dependencies
- Environment validation scripts:
./scripts/verify_env.py
- Pre-built images:
- Experimental recording system::
- automatic generation
experiment_log.jsonRecords:- Complete git commit hash
- CUDA/cuDNN version
- All random seed values
- utilization
--enable-wandbParameter ConnectionsWeights & Biases Service
- automatic generation
Collaborative research proposals
Suggested workflow:
- Create an experimental branch:
git checkout -b exp-[实验代号] - modifications
config.yamlAdding a change note when - utilization
./scripts/snapshot.shGenerate a snapshot of the environment - Adoption of GPQA Diamond benchmarks as a harmonized assessment standard
This answer comes from the articleOpen-Reasoner-Zero: Open Source Large-Scale Reasoning Reinforcement Learning Training PlatformThe
May not be reproduced without permission:AI productivity tools " How to optimize the reproducibility of experiments in academic research scenarios?
