📄 中文摘要
CodeFu-7B 是一个专为竞争编程设计的 70 亿参数模型。通过使用 Group Relative Policy Optimization (GRPO) 和 veRL 训练库，在 Amazon SageMaker 的分布式 Ray 集群中进行训练。该训练库灵活高效，支持多种强化学习算法的扩展，并与现有的大型语言模型基础设施无缝集成。实施过程中涵盖了数据准备、分布式训练设置和全面的可观察性，展示了这种统一方法如何在复杂的强化学习训练工作负载中提供计算规模和开发者体验的双重优势。
在 Amazon SageMaker 训练作业中使用 veRL 和 Ray 训练 CodeFu-7B
出处: Train CodeFu-7B with veRL and Ray on Amazon SageMaker Training jobs
发布: 2026年2月24日
📄 English Summary
Train CodeFu-7B with veRL and Ray on Amazon SageMaker Training jobs
CodeFu-7B is a specialized 7-billion parameter model designed for competitive programming. The training process employs Group Relative Policy Optimization (GRPO) alongside veRL, a flexible and efficient training library for large language models (LLMs), within a distributed Ray cluster managed by Amazon SageMaker training jobs. This library facilitates straightforward extensions of diverse reinforcement learning algorithms and seamless integration with existing LLM infrastructure. The implementation covers data preparation, distributed training setup, and comprehensive observability, showcasing how this unified approach delivers both computational scale and an enhanced developer experience for sophisticated RL training workloads.