TRL: Co-located vLLM for Efficient LLM Training

AI Impact Summary

The TRL team has introduced co-located vLLM integration, fundamentally changing how training and inference utilize GPU resources. Previously, vLLM operated as a separate server, leading to significant idle GPU time and increased hardware demands. This new approach allows vLLM to run alongside the training process, sharing GPUs and eliminating the ‘ping-pong’ effect, resulting in dramatically improved throughput and reduced operational costs, particularly beneficial for online learning setups like GRPO.