Reinforcement learning systems that run inference and training on separate GPU resources must continually sync updated weights, but differing parallel layouts traditionally mandate wasteful gather-and-broadcast cycles. Standard approaches reconstruct complete tensors on training nodes before sending them, generating logical payloads twice the size of the underlying models. For frontier models reaching trillions of parameters, this traditional transfer generates massive data burdens approaching ten or more terabytes per step. Adopting the Reshard collective circumvents full-tensor reconstruction by mapping parameter shards directly between irregular source and destination boundaries.
Key points
Synchronizing frontier reinforcement learning models across isolated training and inferen…: Synchronizing frontier reinforcement learning models across isolated training and inference clusters generates critical communication bottlenecks under traditional refit paradigms.
The Reshard collective solves irregular parallel layout boundaries, mismatched GPU counts…: The Reshard collective solves irregular parallel layout boundaries, mismatched GPU counts, and replication challenges without full-tensor reconstructions.
Developers can access and integrate the NCCL M2N Reshard collective directly through NVID…: Developers can access and integrate the NCCL M2N Reshard collective directly through NVIDIA's nccl-extensions open-source repository.
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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 inference GPUs with the latest parameters generated on the training GPUs. This transfer of model weights between training and inference GPUs is called refit.
Refit involves two defining characteristics that pose challenges. First, inference and training have different computational requirements, so the weights are distributed differently across inference and training instances by having different parallel mappings (data, context, tensor, expert, and pipeline parallelism).
Second, because refit must update all model weights on the inference GPUs, the communication payload is on the order of the total size of the model’s weights. This payload is already very large for the latest frontier models and is expected to continue growing. Together, these characteristics mean that refit requires high-performance communication across a wide…
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