AI BriefingTRL Adds vLLM Co-location Support
Key point
TRL has integrated vLLM into the training process, reducing GPU resource waste and improving GRPO training efficiency.
Details
Online learning algorithms like GRPO have the model generate answers directly as part of training, making the generation step the main bottleneck of the entire training loop.
The existing TRL approach ran vLLM in a separate server mode, splitting GPUs for training and inference. This caused a 'ping-pong' phenomenon where inference GPUs sat idle during training and training GPUs sat idle during inference, leading to wasted resources and increased costs.
The newly introduced Co-location feature runs vLLM within the same distributed process group as the training code. This provides the following benefits:
- Unified execution: Training and inference share the same GPU, efficiently alternating tasks
- Eliminated communication overhead: Runs inline without HTTP API calls, reducing latency
- Scalability support: Compatible with
torchrun, supporting tensor parallelism (TP) and data parallelism (DP) - Simplified deployment: Direct control within the training job, without managing a separate server script
With this update, high-load online training environments like GRPO can achieve high throughput without additional hardware.
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