vERL

Scalable reinforcement learning framework for LLM alignment and post-training

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Overview

vERL is a scalable reinforcement learning framework designed for LLM post-training and alignment. It provides efficient distributed implementations of PPO and other RL algorithms optimized for large language models.

Key Features

  • Highly scalable RL training for LLMs
  • Efficient actor-critic architecture
  • Distributed rollout generation
  • Supports large model sizes with parallelism
  • Integration with HuggingFace models