Video by PyTorch via YouTube
RL post-training has become a critical stage in modern LLM development, but deploying an end-to-end pipeline requires much more than running individual kernels efficiently. Systems must coordinate distributed training, rollout generation, and continuous weight synchronization across multiple software stacks while maintaining correctness and performance.
Joy Song with AMD will present a poster at PyTorch Conference North America on enabling the open source vime RL post-training framework on AMD Instinct GPUs using ROCm.
vime pairs Megatron with vLLM for high throughput rollout generation, forming a tightly coupled loop where model weights are continuously synchronized between the two systems.
Joy will focus on the engineering challenges this brings up, including correctness issues that emerge only during integrated training and rollout execution and how systematic debugging can strengthen open source PyTorch infrastructure on AMD GPUs.
Register for PyTorchCon NA at: https://hubs.la/Q04v4SL60