TRL has introduced co-located vLLM to improve the efficiency of training large language models by allowing both training and inference to run on the same GPUs, eliminating idle time and reducing hardware costs. This integration enhances throughput, simplifies deployment, and makes the system more robust for online learning setups like GRPO. The new approach is supported by a series of performance experiments demonstrating significant speedups compared to traditional server setups.

The article serves as an introduction to VLLM, a framework designed for serving large language models efficiently. It discusses the benefits of using VLLM, including reduced latency and improved resource management, making it suitable for production environments. Key features and implementation steps are also highlighted to assist users in adopting this technology.