project
X-R1 - A low-cost training framework based on reinforcement learning
X-R1 is a low-cost training framework based on reinforcement learning that accelerates the scaling post-training development of large-scale language models. X-R1 trains 0.5B (500 million parameters) R1-Zero models at extremely low cost...
What is X-R1?
X-R1 is a low-cost training framework based on reinforcement learning that accelerates the scaling post-training development of large-scale language models. X-R1 trains a 0.5B (500 million parameters) R1-Zero model at extremely low cost, requiring only four 3090 or 4090 GPUs, with a training time of approximately one hour and a cost of less than $10. X-R1 supports larger-scale models (such as 1.5B, 7B, 32B, etc.) and provides datasets of different sizes to enable fast training loops.
Main functions of X-R1
- Low-cost trainingTraining can be completed in one hour using four 3090/4090 GPUs at a cost of less than $10.
- Model size supportSupports models of different sizes, such as 0.5B, 1.5B, 7B, and 32B.
- DatasetIt provides datasets of different sizes, such as 0.75k, 1.5k, and 7.5k, for fast training loops.
- Log recordingRecord GRPO online sampling data to a log file.
- Scalability and flexibilityIt provides detailed configuration files and training scripts, allowing users to customize according to their needs.
X-R1 Technical Principles
- Reinforcement Learning (RL)X-R1 uses reinforcement learning to optimize the model training process. Based on a defined reward function, the model adjusts its parameters according to the reward signal during training to maximize the cumulative reward. GRPO (Gradient-based Reinforcement Policy Optimization) is used for online sampling, based on a gradient update strategy, to improve training efficiency and model performance.
- Distributed trainingThe X-R1 supports distributed training, using multi-GPU parallel computing to accelerate the training process. Based on configuration files (such as Zero3.yaml), users can flexibly configure the training environment to achieve efficient parallel training. It employs distributed training frameworks such as DeepSpeed to optimize memory usage and computational efficiency.
- Low-cost hardware configurationThe X-R1 focuses on training with common hardware configurations (such as four 3090 or 4090 GPUs) to reduce hardware costs.
- Log monitoringIt integrates tools such as Wandb to enable visual monitoring of the training process, helping users understand the training status in real time.
X-R1 project address
- GitHub repository:https://github.com/dhcode-cpp/X-R1
Application scenarios of X-R1
- Natural Language Processing ResearchIt helps researchers quickly train and optimize language models, and is applicable to tasks such as text generation, translation, and sentiment analysis.
- Enterprise-level AI developmentEnterprises develop customized language models for customer service, content recommendation, and other applications.
- Education and ScholarshipSuitable for educational institutions and researchers to quickly get started for teaching and research, providing flexible configurations and detailed training scripts.
- Open source communityIt supports multiple hardware configurations, making it convenient for developers to quickly develop and optimize language models, and is suitable for contributing to open source projects.
- Creative Writing and Content GenerationGenerate high-quality creative text, such as advertising copy and news reports, to improve content creation efficiency.