NVIDIA 推出 Reshard Collective 加速强化学习权重重配
Accelerating Reinforcement Learning Weight Refit with the Reshard Collective
NVIDIA 推出 Reshard Collective 技术，通过跨 GPU 资源协同刷新强化学习策略模型权重，将权重重配速度提升 3.5 倍。该方案支持在分散的 GPU 架构中并行运行推理与训练，实现训练效率优化。Reshard Collective 可用于强化学习后训练阶段，通过动态权重同步减少延迟。
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…
来源: NVIDIA Technical Blog · developer.nvidia.com