veRL Framework Improvements
发布时间：
Overview
This project involves integrating and improving veRL (Volcano Engine Reinforcement Learning), which is the open-source version of HybridFlow: A Flexible and Efficient RLHF Framework. veRL is a flexible, efficient and production-ready RL training library for large language models (LLMs).
Key Features
- Flexible RL Algorithm Extension: Easy extension of diverse RL algorithms using the hybrid-controller programming model, enabling flexible representation and efficient execution of complex post-training dataflows (e.g., GRPO, PPO)
- Seamless LLM Infrastructure Integration: Modular APIs that decouple computation and data dependencies, enabling seamless integration with existing LLM frameworks (FSDP, Megatron-LM, vLLM, SGLang, etc.)
- Flexible Device Mapping: Support for various placement of models onto different sets of GPUs for efficient resource utilization and scalability
- HuggingFace Integration: Ready integration with popular HuggingFace models
- High Performance: State-of-the-art throughput with SOTA LLM training and inference engine integrations
- Efficient Actor Model Resharding: 3D-HybridEngine eliminates memory redundancy and significantly reduces communication overhead during transitions between training and generation phases
Technologies
- Reinforcement Learning for LLMs
- veRL Framework (HybridFlow)
- PyTorch
- FSDP, Megatron-LM, vLLM, SGLang
- HuggingFace Models
- Distributed Training
Impact
The improvements significantly enhance the efficiency and effectiveness of reinforcement learning-based training for large language models. veRL’s flexible architecture and high-performance design enable faster RLHF training with better resource utilization and scalability.