Verl - RL Training Library for LLMs - Install and Test Locally
AI Summary
Overview
- Video Title: Overview of World Reinforcement Learning Library
- Presenter: Fahad Miraza
Key Points
- Introduction to World
- World is a reinforcement learning (RL) library tailored for post-training of large language models (LLMs).
- Implements hybrid flow architecture for flexibility and efficiency in RLOS development using modern hardware.
- Features integration with popular ML backends (e.g., PyTorch, FSTP, Megatron LM, VLLM).
- Supports multi-GPU and cluster parallelism.
- Installation
- Installed via Docker, recommended for ease of managing dependencies.
- Requires Docker pre-installed on the system.
- Example provided using an NVIDIA RTX H100 GPU and GSM8K dataset for training.
- Training Process
- Utilizes Proximal Policy Optimization (PPO) to enhance model performance beyond standard supervised fine-tuning.
- Details provided about post-training techniques, emphasizing reinforcement learning’s role in refining model responses.
- Command Examples
- Docker pull command to download World image.
- Docker container creation and execution commands provided.
- Procedure for upgrading World and preparing data models.
- Final command for initiating RL training, specifying dataset paths, hyperparameters, and logging info.
- Conclusion
- World facilitates cutting-edge RL research with LLMs, recommended to be installed via Docker.