Accelerating Reinforcement Learning Weight Refit with the Reshard Collective
Accelerating Reinforcement Learning Weight Refit with the Reshard Collective
NVIDIA has launched the Reshard Collective technology, which enhances the speed of reinforcement learning strategy model weight reshuffling by 3.5 times through cross-GPU resource coordinated refreshing. This solution supports parallel running of inference and training in distributed GPU architectures, achieving training efficiency optimization. Reshard Collective can be used in the post-training stage of reinforcement learning, reducing latency through dynamic weight synchronization.
During reinforcement learning (RL) post-training, training trajectories are generated on the fly by running inference with the current policy model. For...
During reinforcement learning (RL) post-training, training trajectories are generated on the fly by running inference with the current policy model. For training efficiency, it is common to run inference and training in parallel on separate GPU resources in a disaggregated setup. Although the policy model is updated only on the training GPUs, the system must periodically refresh the weights on the…
Source: NVIDIA Technical Blog · developer.nvidia.com