In simple English
This library helps researchers train large language models using reinforcement learning. It makes the training process faster and uses fewer computer resources. You can plug it into popular training engines and language models without rewriting your code. It is free to use and open source. You will need powerful graphics cards and technical knowledge to run it.
What it is
Verl is a reinforcement learning framework for large language models that decouples computation and data dependencies. It allows you to run complex post-training algorithms across clusters of graphics processing units.
What it is good for
It is good for AI researchers and engineers who want to train reasoning models or align language models using algorithms like PPO and GRPO. It removes the memory bottlenecks that usually happen when switching between generating text and updating model weights.
- Integrates cleanly with existing tools like PyTorch FSDP and vLLM.
- Supports modern reinforcement learning algorithms such as GRPO and PPO.
- Includes memory-efficient engines that reduce communication overhead across servers.
- Requires multiple high-end graphics processing units with large amounts of memory.
- Steep learning curve for anyone who is not an experienced machine learning engineer.
- Setup involves complex distributed computing configurations and dependencies.
Writing custom, inefficient training loops for reinforcement learning on large language models.
vLLM (a fast and easy-to-use library for serving language models with high throughput)
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git clone https://github.com/verl-project/verl.git cd verl pip install -e .