Detailed Introduction
EasyR1 is an efficient, scalable reinforcement learning (RL) training framework for multimodal models. Built as a clean fork of veRL, EasyR1 incorporates engineering optimizations—such as HybridEngine and vLLM SPMD support—to enable RL training and evaluation for large language and vision-language models. The project includes Docker images, example scripts, and dataset templates to simplify experimentation and deployment.
Main Features
- Multimodal model support: compatible with text and vision-text models and dataset formats.
- Scalable training engine: leverages HybridEngine and distributed strategies for multi-node training.
- Algorithms and training tricks: supports GRPO, DAPO, Reinforce++, and optimizations like padding-free training.
- Engineering and monitoring: provides Docker images, and integrations with Wandb, Mlflow and Tensorboard.
Use Cases
EasyR1 is suitable for research and engineering that require RL-based policy optimization on large or multimodal models: improving multimodal reasoning, training reward models, reproducing RL baselines, and running large-scale multi-node experiments for performance validation.
Technical Features
- vLLM SPMD and custom parallel strategies to reduce memory bottlenecks.
- Dataset examples and model merger scripts for Hugging Face checkpoint interoperability.
- Containerized deployment recipes and Ray multi-node examples for cloud-native execution.
- Open-source under Apache-2.0 with an active ecosystem and multiple reproduction projects.